How to create an empty list in Python

An empty list is a foundational data structure in Python. You need it to collect items dynamically, such as user inputs or data processed within a loop.

You'll explore methods like using [] or the list() constructor. You will also find practical tips, see real-world applications, and get advice to debug common errors.

Creating an empty list with square brackets

empty_list = []
print(empty_list)
print(type(empty_list))
print(len(empty_list))--OUTPUT--[]
<class 'list'>
0

Using square brackets, [], is the most direct and Pythonic way to create an empty list. This literal syntax is preferred for its clarity and efficiency. The code then verifies the properties of the newly created list.

• The type() function confirms that Python correctly interprets [] as a list object.
• The len() function shows the list starts with a length of zero, ready to be populated.

Basic methods for creating empty lists

Beyond the straightforward [] syntax, you can also create empty lists using the list() constructor, list comprehensions, or the multiplication operator.

Using the list() constructor

empty_list = list()
print(empty_list)
print(type(empty_list))
print(len(empty_list))--OUTPUT--[]
<class 'list'>
0

You can also initialize an empty list using the list() constructor. Calling this built-in function without any arguments creates a new, empty list object. It’s functionally the same as using [] for this specific task, though slightly less common.

• While [] is often favored for its speed and clarity, the list() constructor is powerful for converting other iterables—like tuples or strings—into lists.
• For simply creating an empty list, either method works, but you'll see [] used more frequently in practice.

Creating empty lists with list comprehension

empty_list = [x for x in range(0)]
print(empty_list)
empty_list2 = [item for item in []]
print(empty_list2)--OUTPUT--[]
[]

List comprehensions offer a powerful way to build lists, and you can use them to create an empty one by iterating over a sequence with no items. Since the sequence is empty, the expression inside the comprehension never runs.

• The code [x for x in range(0)] works because range(0) is an empty sequence.
• Likewise, [item for item in []] iterates over an empty list, which also produces an empty list.

While this method works, it's more verbose than using [] or list() for this specific task.

Using multiplication with empty lists

empty_list = [] * 10  # Still creates just an empty list
print(empty_list)
another_empty = list() * 5
print(another_empty)--OUTPUT--[]
[]

You can also use the multiplication operator * to repeat a list's contents. When you apply it to an empty list, you're essentially telling Python to repeat nothing, so the result is predictably empty.

• Multiplying [] by any integer, as in [] * 10, logically results in another empty list because there are no elements to duplicate.

While it's an interesting quirk of how list multiplication works, it's not a common or practical way to initialize an empty list compared to [] or list().

Advanced techniques for empty lists

While the basic methods are great for general use, you'll sometimes need more advanced techniques for type safety, performance optimization, or immutability.

Creating typed empty lists with annotations

from typing import List

# Type-annotated empty lists
int_list: List[int] = []
str_list: List[str] = list()
print(f"Empty integer list: {int_list}")
print(f"Empty string list: {str_list}")--OUTPUT--Empty integer list: []
Empty string list: []

For better code clarity and maintainability, you can use type annotations. This practice doesn't change how the code runs—Python won't enforce the type—but it provides valuable hints to developers and static analysis tools about what kind of data the list should hold.

• You import List from the typing module to declare the list's intended content type, such as List[int] for integers or List[str] for strings.
• This helps catch potential bugs early and improves autocompletion in your editor, making your code more robust.

Pre-allocating memory for empty lists

import sys

regular_empty = []
preallocated_empty = [None] * 10
preallocated_empty.clear()

print(f"Regular size: {sys.getsizeof(regular_empty)} bytes")
print(f"Pre-allocated size: {sys.getsizeof(preallocated_empty)} bytes")--OUTPUT--Regular size: 56 bytes
Pre-allocated size: 104 bytes

For performance-critical code where a list will grow significantly, you can pre-allocate memory. This technique helps you avoid the overhead of dynamically resizing the list each time you add an element, which can be a slow process.

• You can do this by creating a list of a certain size, like [None] * 10, and then immediately emptying it with the .clear() method.
• Although the list is empty, it retains the allocated memory, making future appends faster until that reserved capacity is filled.

Creating immutable empty sequences

empty_tuple = ()
empty_frozenset = frozenset()
empty_list = []

print(f"Tuple: {empty_tuple}, Frozenset: {empty_frozenset}, List: {empty_list}")
print(f"Tuple is mutable: {hasattr(empty_tuple, 'append')}")
print(f"List is mutable: {hasattr(empty_list, 'append')}")--OUTPUT--Tuple: (), Frozenset: frozenset(), List: []
Tuple is mutable: False
List is mutable: True

Sometimes you need a sequence that can't be changed after it's created. While lists are mutable, Python offers immutable alternatives like tuples and frozensets. This is useful when you need to guarantee that a collection of items remains constant throughout your program.

• You can create an empty tuple with ().
• An empty frozenset is made using the frozenset() constructor.

The code confirms this immutability by using hasattr() to check for an append method. Since tuples lack methods that would alter them, they serve as a safe, unchangeable container for your data.

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Common errors and challenges

Even though creating an empty list is simple, you'll want to be aware of common pitfalls with function arguments, indexing, and adding items.

Avoiding mutable default arguments with empty list

It's a classic Python "gotcha": using an empty list like [] as a default function argument. Because Python creates this default list only once, it gets shared and modified by every call. The code below shows exactly how this can go wrong.

def add_item(item, my_list=[]):
   my_list.append(item)
   return my_list

result1 = add_item("apple")
result2 = add_item("banana")

print(result1)  # Expected: ["apple"]
print(result2)  # Expected: ["banana"]

The default list my_list=[] is created once and shared across all calls to the add_item() function. This means the second call appends to the same list as the first, leading to unexpected results. The code below shows the correct approach.

def add_item(item, my_list=None):
   if my_list is None:
       my_list = []
   my_list.append(item)
   return my_list

result1 = add_item("apple")
result2 = add_item("banana")

print(result1)  # ["apple"]
print(result2)  # ["banana"]

The solution is to set the default argument to None. Inside the function, you check if the argument is None and create a new empty list if it is. This simple pattern guarantees each function call gets a fresh, independent list, preventing the shared state bug. It's a crucial habit to adopt whenever you're defining functions with mutable default arguments like lists or dictionaries.

Handling index errors with empty lists

Accessing an item in an empty list is a guaranteed way to get an IndexError. Because the list contains nothing, there's no element at index 0 or any other position. The code below triggers this error when trying to fetch the first item.

def get_first_item(my_list):
   return my_list[0]

empty_list = []
first_item = get_first_item(empty_list)
print(f"First item: {first_item}")

The get_first_item function fails because it directly accesses my_list[0]. Since the list is empty, there's no element at that index, which raises an IndexError. The code below demonstrates how to prevent this error.

def get_first_item(my_list):
   if my_list:  # Check if list is not empty
       return my_list[0]
   return None

empty_list = []
first_item = get_first_item(empty_list)
print(f"First item: {first_item}")

To fix this, you check if the list has content before accessing an index. The if my_list: statement leverages a Python feature where empty lists evaluate to False. This guard clause lets the function safely return None instead of raising an IndexError. It's a crucial check whenever you're handling lists that might be empty, especially when they are populated dynamically from user input or external data sources.

Understanding append() vs extend() with empty lists

A common mistake is confusing append() with extend(). While both add to a list, append() adds its entire argument as a single element. This can lead to an unwanted nested list instead of a flat one. The following code demonstrates this error.

numbers = []
more_numbers = [1, 2, 3]
numbers.append(more_numbers)
print(numbers)  # Expected: [1, 2, 3]

Instead of adding each number, the append() method treats the entire more_numbers list as a single item. This creates a nested list like [[1, 2, 3]]. The code below shows the right way to combine them.

numbers = []
more_numbers = [1, 2, 3]
numbers.extend(more_numbers)
print(numbers)  # [1, 2, 3]

The extend() method is the right tool for the job. It unpacks the more_numbers list and adds each item individually, creating a single, flat list. This is what you want when merging collections.

Always use extend() to add all items from an iterable. In contrast, append() adds the entire iterable as a single, nested element. This distinction is key when you're combining lists.

Real-world applications

Once you know how to avoid the common pitfalls, you can confidently use empty lists to build powerful, real-world tools.

Using an empty list for data collection

An empty list is the perfect starting point for gathering data dynamically, such as collecting sensor readings over time.

data_points = []  # Start with an empty list

# Collect temperature readings
for hour in range(24):
   temperature = 20 + (hour % 10)  # Simulated temperature reading
   data_points.append(temperature)
   
print(f"Collected {len(data_points)} temperature readings")
print(f"Average temperature: {sum(data_points)/len(data_points):.1f}°C")

This code starts by creating an empty list called data_points to act as a blank slate for collecting information. A for loop then runs 24 times, simulating the collection of hourly temperature data over a day.

• A new temperature value is simulated with a simple calculation.
• The append() method adds this new value to the end of the data_points list.

After the loop completes, the list is no longer empty. It holds all 24 simulated readings, demonstrating a common pattern where you start with nothing and build a dataset piece by piece.

Implementing a simple FIFO buffer with list

An empty list is also a great foundation for a First-In, First-Out (FIFO) buffer, a simple queue that keeps a running list of recent items by discarding the oldest as new ones arrive.

class SimpleBuffer:
   def __init__(self, max_size=5):
       self.buffer = []  # Start with an empty list
       self.max_size = max_size
       
   def add(self, item):
       self.buffer.append(item)
       if len(self.buffer) > self.max_size:
           self.buffer.pop(0)  # Remove oldest item
           
# Using our buffer
message_buffer = SimpleBuffer(3)
for message in ["Hello", "How", "Are", "You", "Today"]:
   message_buffer.add(message)
   
print(f"Current buffer: {message_buffer.buffer}")

The SimpleBuffer class uses an empty list to manage a collection with a fixed capacity. When you create an instance, like SimpleBuffer(3), you set its maximum size.

• The add() method uses append() to add a new item to the end of the list.
• If the list’s length exceeds the defined max_size, the code calls pop(0) to remove the element at the very first index.

This process ensures the buffer always maintains its size limit, effectively keeping a sliding window of the most recent entries.

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