How to Create a Data Splitter Class in Python

This code defines a DataSplitter class that splits data by index, into chunks, or by a predicate, demonstrating OOP principles in Python.

Medium Python 3.9+ Aug 9, 2026 OOP & classes 13 views 0 copies

class data-splitting slicing list-comprehension

Python code

28 lines

Python 3.9+

class DataSplitter:
    def __init__(self, data):
        self.data = list(data)
    
    def split_by_index(self, index):
        return self.data[:index], self.data[index:]
    
    def split_into_chunks(self, chunk_size):
        return [self.data[i:i + chunk_size] for i in range(0, len(self.data), chunk_size)]
    
    def split_by_predicate(self, predicate):
        matching = [item for item in self.data if predicate(item)]
        non_matching = [item for item in self.data if not predicate(item)]
        return matching, non_matching


if __name__ == "__main__":
    numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    splitter = DataSplitter(numbers)
    
    first, second = splitter.split_by_index(4)
    print(f"Split at index 4: {first} | {second}")
    
    chunks = splitter.split_into_chunks(3)
    print(f"Chunks of size 3: {chunks}")
    
    evens, odds = splitter.split_by_predicate(lambda x: x % 2 == 0)
    print(f"Evens: {evens} | Odds: {odds}")

Output

stdout

Split at index 4: [1, 2, 3, 4] | [5, 6, 7, 8, 9, 10]
Chunks of size 3: [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10]]
Evens: [2, 4, 6, 8, 10] | Odds: [1, 3, 5, 7, 9]

How it works

The __init__ method converts the input to a list, allowing any iterable to be used. split_by_index uses slicing to return two new lists at the given index. split_into_chunks uses list comprehension with a range step to create fixed-size chunks. split_by_predicate uses list comprehensions with the predicate to separate matching and non-matching items, and the if __name__ == "__main__" guard runs the example only when executed directly.

Common mistakes

Forgetting to convert input to a list, which breaks for generators or sets.
Assuming slice indices are zero-based incorrectly.
Using a predicate that mutates the data unexpectedly.
Not handling chunk_size of zero or negative.

Variations

Add a `max_chunks` parameter to limit the number of chunks.
Use `itertools.islice` to split iterators lazily without creating a full list.

Real-world use cases

Splitting a dataset into training and testing sets by index for machine learning.
Batch processing: dividing a large list of items into chunks for parallel or sequential tasks.
Filtering log entries into error and non-error categories based on a predicate.

How to Create a Data Splitter Class in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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Python code

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How it works

Common mistakes

Variations

Real-world use cases

More from OOP & classes

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