Splitting a list into equal-sized chunks is the most common operation in Python. It is helpful when we have to process large datasets in small parts of the data. In this blog, we will discuss several ways to split a list into equal-sized chunks in Python with examples for each, common errors, advanced splitting methods, and how to avoid the errors with the best practices.
Table of Contents:
Methods to Split a List into Evenly Sized Chunks in Python
Below we have discussed a few methods to split a list into evenly sized chunks in Python:
Method 1: Using a Loop with List Slicing in Python
Splitting a list into evenly sized chunks in Python can be done using a loop and through list slicing with the provided chunk size. This technique iterates through the list in steps of the chunk size to obtain each chunk. It is simple to implement and requires no external libraries.
Example:
Output:
Explanation: In this code, the for loop is used for slicing the given list into an equal number of chunks each chunk is stored one at a time.
Method 2: Using List Comprehension with range() in Python
The list comprehension method using range() in Python allows splitting a list into chunks. It steps through the list with a step size that is the same as the chunk size, thereby producing sublists.
Example:
Output:
Explanation: The range(0, len(lst), chunk_size) creates the starting indices of each chunk, and the for loop iterates the chunks and prints each chunk.
The itertools.islice() method provides efficient slicing of an iterable without making many copies of the list and returns chunks of a given size. This is memory-efficient since it processes elements gradually and is best suited for processing large datasets.
Example:
Output:
Explanation: The itertools.islice() split the list into chunks, and the islice(it, chunk_size) creates an iterator that returns a chunk of size chunk_size.
Method 4: Using NumPy (for large arrays/lists) in Python
In Python, the array_split() function in NumPy is used to divide a large list into almost evenly sized chunks. The function has the benefit of ensuring chunks are as evenly divided, even though the length of the list may not be divisible by the size of the chunks, further, NumPy needs to be installed (pip install numpy) before utilization.
Example:
Output:
Explanation: This code uses NumPy’s array_split() function to split the list lst into chunks of size chunk_size, and the np.array_split() splits the list into nearly equal parts and returns them as separate NumPy arrays.
Method 5: Using a Generator Function in Python
The generator method is similar to a list comprehension method with the only exception that it is more memory-efficient as it only stores one chunk at a time while processing.
Example:
Output:
Explanation: The list is split into small chunks of a specified size the chunk size is specified as 3, and it uses the yield statement inside the function to return each chunk without storing them all at once in the memory.
The more_itertools.chunked method splits the list into chunks based on the given size. This method is useful while working on big data sets.
Example:
Output:
Explanation: The more_itertools.chunked() divides the iterable into chunks of the specified size (chunk_size).
If the size of the iterable is not perfectly divisible by the chunk size, the last chunk will contain the remaining element.
Common Mistakes While Splitting the List and How to Avoid Them
While slicing or breaking a list into smaller pieces, mistakes may arise. Below are a few common mistakes and points to how to avoid them:
1. Indexing Errors
Slicing a list with the wrong range can lead to missing elements. So, always double-check your indexes to ensure the accuracy.
Example:
Output:
Explanation: In this example, the sublist_1 retrieves items from indexes 1 to 3, and the sublist_2 retrieves items from indexes 1 to 2, omitting courses which is improperly indexed.
2. Import Errors while using External Libraries
Some functions or methods need external packages like NumPy or itertools to be imported. So, check always if these libraries were imported or not.
Example:
Output:
Explanation: In this code, the np.array_split() splits the list into 3 equal parts, but NumPy must be installed first to avoid errors.
3. Edge Case Handling
Slicing may create unexpected results when the size of the list is lower than the chunk size. So, make sure to determine the original size of the list before splitting.
Example:
Output:
Explanation: In this code, since the elements of the advantages list are less than the chunk_size, therefore the whole list is assumed as one chunk to handle the edge case.
Best Practices for Splitting the List in Python
- Slicing produces new copies of the original list and doesn’t change it. Hence, if you don’t want to change the original list then be careful.
- When dealing with a large list, duplicate the data a few times for improved performance and use the iterators or generators for speedup.
- Special cases are empty lists, lists containing one item, or lists that don’t divide evenly. Handling these special cases properly will keep your function away from crashing.
- Using try and except to catch whatever errors are likely to occur, and input validation prior to any split function will avoid the errors.
Advanced List Splitting Techniques in Python
At times, you might want more control during splitting lists. The following are some advanced ways to split a list in Python:
Splitting a List at Specific Indices in Python
You can manually split the list as per your need by providing the indices.
Example:
Output:
Explanation: This function splits the list at the given indices.
Splitting a List into Equal Parts in Python
NumPy is appropriate for equal slice pieces that provide you with equal linear size in linear time.
Example:
Output:
Explanation: In this code, the list is split into 3 equal parts.
Splitting a List into Half in Python
You can easily use slicing for a simple two-way split of the lists.
Example:
Output:
Explanation: This function splits the list into two halves.
Comparison of Methods to Split a List into Evenly-Sized Chunks in Python
|
Methods |
Readability | Memory Efficiency |
Speed |
Dependencies |
| List Slicing | Less readable as it requires manual slicing | Low | Moderate speed | No dependencies |
| List Comprehension | High, concise, and easy-to-read | Low | Moderate, as it iterates over the list | No additional dependencies |
| Generator Function | Moderate | High | Fast | No additional dependencies |
| Numpy | High but requires an understanding of the numpy modules | Low | Fast | Requires Numpy library |
| itertools.iskice() | High readability | High | Fast, highly efficient for slicing | Requires itertools |
| more_itertools.chunked() | Easy to read | Low | Fast and optimized | Requires more-itertools library |
Conclusion
Splitting the lists by various methods has various advantages. Also, the application of methods such as list slicing and list comprehension that are simple to code but not memory-efficient, and the application of generator functions and itertools that are faster and use less memory give us greater efficiency. The above methods help us to split the list into chunks of equal size effectively in Python.
FAQs
1. How do I split a list into chunks in Python?
You can use the list slicing or itertools to partition the list into smaller chunks.
2. Which method is best for large lists?
The Generators and NumPy are the best methods for the efficient management of large lists.
3. Which libraries help in handling large lists efficiently?
NumPy and itertools are usually used for handling large lists efficiently.
4. How would I split a string into a list in Python?
You can use string.split on the string, which will create a list based on the separator in Python.
5. Is there a way to split a list without creating new lists in memory?
Yes, using a generator function allows you to process chunks without storing all chunks at once.