How to create an empty list in Python

Creating empty lists in Python provides a foundation for building dynamic data structures. Whether you're developing algorithms, managing collections, or processing data, understanding how to initialize empty lists correctly helps you write more efficient code.

This guide covers essential techniques for creating empty lists, with practical examples and debugging tips created with Claude, an AI assistant built by Anthropic.

Creating an empty list with square brackets

empty_list = []
print(empty_list)
print(type(empty_list))
print(len(empty_list))

[]
<class 'list'>
0

Square bracket notation offers the most straightforward way to create an empty list in Python. The syntax empty_list = [] initializes a list with zero elements, providing a clean slate for adding data later. This approach uses minimal memory since no pre-allocation occurs.

The code demonstrates three key aspects of empty lists:

• The print() output shows empty square brackets, confirming the list contains no elements
• The type() verification ensures we've created a proper list object
• The len() function returns 0, indicating the list is truly empty

Basic methods for creating empty lists

Beyond square brackets, Python provides several alternative approaches to create empty lists—including the list() constructor, list comprehension syntax, and multiplication operations.

Using the list() constructor

empty_list = list()
print(empty_list)
print(type(empty_list))
print(len(empty_list))

[]
<class 'list'>
0

The list() constructor provides a built-in function approach to create empty lists. While it achieves the same result as square brackets, this method explicitly calls Python's list creation functionality.

• The constructor returns an empty list object that's ready for storing elements
• It's particularly useful when working with type hints or when you need to make the code's intent crystal clear
• Some developers prefer this syntax because it mirrors how you'd create other built-in objects like dict() or set()

The output demonstrates that list() creates an identical empty list to the square bracket method. You'll see empty brackets printed, confirmation of the list type, and a length of zero.

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)

[]
[]

List comprehension offers an elegant way to create empty lists by iterating over empty sequences. Both examples demonstrate this by using different empty iterables: range(0) and [].

• The first example uses [x for x in range(0)] which creates an empty list because range(0) generates no values to iterate over
• The second approach [item for item in []] achieves the same result by iterating over an empty list
• While these methods work perfectly, they're more commonly used to demonstrate list comprehension concepts than for practical empty list creation

Both approaches produce identical empty lists. However, for creating simple empty lists, the square bracket notation or list() constructor remains more straightforward and readable.

Using multiplication with empty lists

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

[]
[]

Multiplying an empty list with a number in Python doesn't replicate the list. The expression [] * 10 or list() * 5 still produces an empty list, unlike multiplying a list containing elements which would create copies.

• The multiplication operator * with empty lists creates no new elements since there's nothing to replicate
• This behavior differs from multiplying non-empty lists, where Python would create multiple copies of the existing elements
• Both square bracket and constructor methods exhibit identical behavior when multiplied

Understanding this nuance helps prevent confusion when working with list operations. It's particularly relevant when dynamically creating lists based on variable multiplication factors.

Advanced techniques for empty lists

Beyond the basic empty list creation methods, Python offers specialized techniques for type safety, memory optimization, and data immutability that enhance your list operations.

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}")

Empty integer list: []
Empty string list: []

Type annotations in Python help catch potential errors before your code runs. The List[type] syntax from the typing module specifies which data type the list should contain, creating a contract for type checkers to enforce.

• Using List[int] indicates the list should only contain integers
• The List[str] annotation restricts the list to string elements
• These annotations don't affect runtime behavior. They serve as documentation and enable static type checking tools to validate your code

While both examples create empty lists, the type hints communicate your intent to other developers and help maintain code quality as your project grows. Type checkers will flag attempts to add incompatible data types, reducing bugs in production.

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")

Regular size: 56 bytes
Pre-allocated size: 104 bytes

Pre-allocating memory for empty lists in Python reveals an interesting memory management behavior. When you create a list with [None] * 10 and then clear it using clear(), Python retains the originally allocated memory space instead of releasing it.

• The regular empty list (regular_empty = []) uses minimal memory at 56 bytes
• The pre-allocated list maintains its larger memory footprint of 104 bytes even after clearing all elements
• This retained memory can improve performance when you plan to add many elements later. The list won't need to resize as frequently

Understanding this behavior helps you make informed decisions about list initialization. Choose pre-allocation when you know the approximate final size and want to optimize for repeated append operations.

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')}")

Tuple: (), Frozenset: frozenset(), List: []
Tuple is mutable: False
List is mutable: True

Python offers immutable alternatives to lists when you need sequences that can't be modified after creation. The code demonstrates three empty sequence types: tuples with (), frozensets with frozenset(), and regular lists with [].

• Tuples and frozensets prevent accidental data modifications. They lack methods like append or remove that would change their contents
• The hasattr() function reveals this key difference. It checks whether an object has specific attributes or methods
• Lists remain mutable. You can freely add, remove, or modify their elements after creation

Choose immutable sequences when you need to ensure data integrity or create hashable objects for use in sets and dictionaries. They provide a safeguard against unintended changes in your code.

Using an empty list for data collection

Empty lists provide an efficient foundation for collecting and analyzing real-time data streams, as demonstrated in this temperature monitoring example that builds a dataset 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 simulates a 24-hour temperature monitoring system. It initializes an empty list called data_points and uses a for loop to iterate through each hour. The temperature calculation 20 + (hour % 10) creates a repeating pattern between 20-29°C, simulating daily temperature fluctuations.

The program performs two key operations with the collected data:

• Counts total readings using len(data_points)
• Calculates the average temperature by dividing the sum of all readings by their count, formatted to one decimal place

This pattern demonstrates how empty lists serve as flexible containers for accumulating and analyzing sequential data over time.

Implementing a simple FIFO buffer with list

Empty lists enable efficient First-In-First-Out (FIFO) buffer implementations that maintain a fixed-size queue of the most recent items, as shown in this SimpleBuffer class example.

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 implements a fixed-size data structure that automatically removes the oldest item when it reaches capacity. It initializes with an empty list and a specified maximum size.

When you add items using the add() method, the buffer maintains only the most recent elements up to max_size. The class uses append() to add new items to the end and pop(0) to remove the oldest item from the beginning when necessary.

• The example creates a buffer limited to 3 items
• It processes five messages sequentially
• The final output will contain only the three most recent messages

This pattern proves useful for managing recent events, logging, or maintaining sliding windows of data.

Common errors and challenges

Python developers frequently encounter three critical challenges when working with empty lists: mutable defaults, index handling, and method confusion.

Avoiding mutable default arguments with empty list

Python's mutable default arguments create a common trap for developers working with empty lists. When you define a function parameter as my_list=[], Python creates the list only once at function definition time. This shared list persists between function calls, leading to unexpected behavior. Let's examine this issue in action:

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 add_item() function shares the same list across all calls because Python creates the default list only once. Each function call modifies this shared list instead of creating a fresh one. The following code demonstrates the proper implementation.

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 improved version uses None as the default argument instead of an empty list. This approach creates a fresh list for each function call, preventing the shared mutable state problem that caused unexpected results in the original code.

• Always initialize mutable defaults like lists inside the function body
• Use None as a sentinel value to trigger new list creation
• Watch for this issue when working with any mutable default arguments including dictionaries and sets

This pattern appears frequently in data processing functions and API implementations. The fix ensures each function call maintains its own independent state without interference from previous calls.

Handling index errors with empty lists

Accessing elements in empty lists triggers Python's IndexError exception. This common issue occurs when developers attempt to retrieve items from a list containing no elements. The code below demonstrates what happens when we try to access the first item of an empty list.

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 code fails because get_first_item() attempts to access index 0 of an empty list. Python raises an IndexError since no elements exist to retrieve. The following code demonstrates a safer approach to handle empty list scenarios.

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}")

The improved get_first_item() function prevents index errors by checking if the list contains elements before attempting to access them. Using Python's built-in truthiness, the condition if my_list evaluates to False for empty lists and True for lists with elements.

• Watch for this pattern when processing user inputs or API responses that might return empty lists
• Consider using the len() function for more explicit length checks
• Return meaningful default values (None, empty string, or zero) based on your application's needs

Understanding append() vs extend() with empty lists

Python developers often confuse append() and extend() when adding elements to empty lists. The append() method adds its argument as a single item while extend() adds each element individually. This distinction becomes crucial when working with nested data structures.

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

The code produces unexpected results because append() adds the entire list more_numbers as a single nested element instead of incorporating its individual values. The output becomes [[1, 2, 3]] rather than the expected [1, 2, 3]. The following example demonstrates the correct approach.

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

The extend() method adds each element from an iterable individually to your list. This differs from append(), which treats the entire input as a single item. Using extend() with more_numbers adds 1, 2, and 3 as separate elements to the numbers list.

• Watch for this issue when combining lists or processing data from APIs that return nested structures
• Remember that extend() only works with iterables. For single items, append() remains the right choice
• Consider using the plus operator (+) as an alternative for list concatenation when you need a new list instead of modifying an existing one