Split a List Into Evenly Sized Chunks in Python Using Yield

In this blog, we will explore a Python program to split a list into evenly sized chunks using yield. We will dive into the step-by-step logic and operation of the code, providing a clear understanding of how to implement this approach. 

In the world of programming, it is often necessary to manipulate lists and divide them into smaller, more manageable chunks. This can be particularly useful when dealing with large datasets or when performing operations that require processing smaller portions of a list at a time. In Python, there are various approaches to achieve this, and one powerful technique is using the yield keyword in combination with recursion.

So, let’s get started and split a list into evenly sized chunks in Python using yield.

Python Program to Split a List Into Evenly Sized Chunks Using Yield

# Python Program to Split a List Into Evenly Sized Chunks Using Yield

my_list = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]
 
# Yield successive n-sized
# chunks from l.
def divide_chunks(l, n):
     
    # looping till length l
    for i in range(0, len(l), n):
        yield l[i:i + n]
 
# How many elements each
# list should have
n = 5

x = list(divide_chunks(my_list, n))
print (x)

Code Explanation

Defining the list to be split

Initialize the list my_list with a sequence of elements [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15].

Defining the function to split the list into chunks

We define a function divide_chunks(l, n) that takes two arguments: the list l to be split and the chunk size n. Within the function, use a for loop to iterate over the range of indices from 0 to the length of the list l, with a step size of n. Within each iteration, use slicing (l[i:i+n]) to extract a chunk of elements from the list. Use the yield keyword to generate the chunk as a generator object.

Determine the chunk size

Next, we assign the desired chunk size to the variable n. In this example, n is set to 5.

Splitting the list into chunks and store the result

Create a new list x and assign it the result of calling the list() function on the generator object obtained from divide_chunks(my_list, n). The list() function consumes the generator and generates a list of chunks.

Printing the result

Use the print() function to display the resulting list x, which contains the chunks of the original list my_list.

Output:

[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]]

The original list, my_list, contains the elements [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]. The function divide_chunks() is called with my_list and a chunk size of 5. The function uses the yield keyword to generate chunks of size 5 from the original list. It splits the list into three chunks: [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], and [11, 12, 13, 14, 15]. The generator object is then converted to a list using the list() function and stored in the variable x.

Finally, the contents of x are printed using the print() function, resulting in the output: [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]]. This demonstrates the successful splitting of the original list into evenly sized chunks of size 5.

Here are a few alternative methods:

Using for Loop:

With a for loop and slicing, we can iterate over the list and extract chunks of the desired size. This method is simple and intuitive to understand. It works well for small to medium-sized lists.

Using List Comprehension:

List comprehension offers a concise way to split a list into chunks using slicing notation. It creates a new list by iterating over the original list and extracting chunks of the desired size. This method is compact and readable, suitable for situations where code brevity is preferred.

Using NumPy:

NumPy, a powerful library for numerical computations, provides the array_split function to split arrays into evenly sized chunks. It is efficient for handling large datasets and performing complex numerical operations. This method is advantageous when dealing with numerical data and performing subsequent calculations.

Using itertools:

The islice function from the itertools module can be used to split a list into chunks. It offers various iteration tools for efficient and memory-friendly operations. This method is beneficial when working with iterators or when more advanced iteration functionalities are required.

Using Collections:

The deque class from the collections module can be utilized to split a list into chunks efficiently. Deque provides efficient operations for adding and removing elements from both ends. This method is useful when frequent appending or popping of elements is required during the splitting process.

In this tutorial, we used the yield method with a generator as it generates chunks on the fly without storing the entire result in memory. This is particularly useful when working with large lists or streaming data. The generator-based approach is lazy, meaning it generates chunks only when required. This can be advantageous when dealing with large datasets, as it avoids unnecessary computations. It also allows you to iterate over the chunks one at a time, which can be useful for scenarios where processing one chunk at a time is more efficient or desirable.

Conclusion

In this tutorial, the code presented demonstrates an effective approach to splitting a list into evenly sized chunks using the yield keyword in Python. By utilizing a generator function, we can efficiently divide a given list into smaller, more manageable portions. This technique is particularly useful when working with large datasets or when processing data in batches.

Splitting a list into evenly sized chunks is a common operation in various applications, such as parallel processing, data partitioning, or batch processing. Understanding how to accomplish this task using the yield keyword provides programmers with a valuable tool for efficiently managing and manipulating data.

By grasping the concept and implementation presented in this code, Python developers can leverage this technique to enhance their programs’ performance and handle large datasets more effectively.

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