Checking If a List or NumPy Array is Empty in Python

Introduction

In programming, it’s often necessary to determine whether a data structure like a list or an array contains any elements. This operation can be crucial for flow control and ensuring that algorithms operate on non-empty collections. In Python, lists and NumPy arrays are commonly used data structures where this check is frequently needed.

This tutorial will guide you through different methods of checking if these structures are empty, covering standard Python lists and NumPy arrays specifically. We’ll explore both idiomatic Python approaches and special considerations for handling NumPy arrays.

Checking if a List is Empty

Using Implicit Boolean Value Testing

In Python, an empty list evaluates to False in a boolean context. This behavior allows us to check the emptiness of a list using simple conditional statements:

a = []

# Check if 'a' is empty
if not a:
    print("List is empty")
else:
    print("List is not empty")

This method is considered Pythonic because it leverages the language’s design philosophy, which encourages readability and simplicity.

Explicitly Checking Length

Another way to determine if a list is empty is by checking its length:

a = []

# Check if 'a' is empty using len()
if len(a) == 0:
    print("List is empty")
else:
    print("List is not empty")

While this approach explicitly checks for an empty list, it may be less concise than the implicit boolean test. However, some developers prefer explicit checks for clarity.

Checking if a NumPy Array is Empty

NumPy arrays require special consideration when checking for emptiness due to differences in how they handle truth value testing compared to standard Python lists.

Issues with Pythonic Methods

Using if array: on a NumPy array can lead to unexpected behavior or errors:

import numpy as np

x = np.array([0, 1])

# Attempting the "Pythonic" way may result in an error for multi-element arrays
try:
    if x:
        print("Array is not empty")
except ValueError as e:
    print(e)

This raises a ValueError because NumPy attempts to evaluate all elements simultaneously, which doesn’t make sense for truth value testing.

Using the Size Attribute

The recommended approach for checking emptiness in NumPy arrays is to use the .size attribute:

import numpy as np

x = np.array([0, 1])

# Correct method using .size
if x.size:
    print("Array is not empty")
else:
    print("Array is empty")

y = np.zeros((1, 0))

# Another example with a different array shape
if y.size:
    print("Array is not empty")
else:
    print("Array is empty")

The .size attribute returns the total number of elements in the array, providing an unambiguous way to determine if it’s empty.

Handling Mixed Input Types

In situations where input could be either a Python list or a NumPy array, you can combine methods to ensure compatibility:

import numpy as np

def is_empty(collection):
    if isinstance(collection, (list, np.ndarray)):
        return len(collection) == 0
    return False

a = []
b = np.array([])

print(is_empty(a))  # Output: True
print(is_empty(b))  # Output: True

This function checks the type of the collection and applies the appropriate method.

Conclusion

Checking if a list or NumPy array is empty in Python can be done using different approaches. For lists, both implicit boolean testing and explicit length checking are viable options. However, for NumPy arrays, it’s crucial to use the .size attribute due to differences in how truth value testing is handled.

By understanding these techniques, you can ensure robust and idiomatic handling of empty data structures in your Python applications.