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How to Split a List into Chunks in Python
Split a list into fixed-size sublists using a simple list comprehension with slicing.
def chunk_list(lst, size):
"""Split a list into sublists of given size."""
return [lst[i:i + size] for i in range(0, len(lst), size)]
if __name__ == "__main__":
sample = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
print(chunk_list(sample, 3))How to Group a List into Chunks in Python
Split a list into smaller groups of a fixed size using a reusable function with a default parameter.
def make_groups(numbers, group_size=2):
"""Splits a list into smaller groups of a given size."""
groups = []
for i in range(0, len(numbers), group_size):
groups.append(numbers[i:i + group_size])
return groups
if __name__ == "__main__":
data = [1, 2, 3, 4, 5, 6, 7]
print("Default size…Chunk Large File Upload Simulation by Blocks in Python
A Python script reads a large binary file in fixed-size chunks and simulates a block-by-block upload with per-chunk SHA256 hashing.
import os
import hashlib
from pathlib import Path
def read_file_in_chunks(file_path, chunk_size=8196):
"""Yield chunks of a file as bytes."""
with open(file_path, 'rb') as f:
while chunk := f.read(chunk_size):
yield chunk
def simulate_chunked_upload(file_path, chunk_size=8196):
"""S…How to Compute File SHA256 Hash with hashlib in Python
Compute the SHA256 hash of a file by reading it in chunks with hashlib and Path.open.
import hashlib
from pathlib import Path
def sha256_file(file_path: Path) -> str:
sha256_hash = hashlib.sha256()
with file_path.open("rb") as f:
for chunk in iter(lambda: f.read(4096), b""):
sha256_hash.update(chunk)
return sha256_hash.hexdigest()
if __name__ == "__main__":
demo_fi…How to Stream Large CSV Files in Python
Process a large CSV file in memory-efficient chunks using Python's csv module, yielding batches of rows instead of loading everything at once.
import csv
from pathlib import Path
def process_csv_in_chunks(file_path, chunk_size=1000):
"""Yield rows from a large CSV file in chunks without loading all into memory."""
with open(file_path, 'r', newline='') as f:
reader = csv.DictReader(f)
chunk = []
for row in reader:
…Read Parquet-Like Columnar CSV Chunks in Python
A Python generator that reads a CSV file column-by-column, yielding dictionary chunks where each key points to a list of values—mirroring how Parquet stores data columnar.
```python
import csv
from pathlib import Path
from typing import Iterator, List
def read_parquet_like_columnar(csv_path: str, column_names: List[str], chunk_size: int = 2) -> Iterator[dict]:
"""Read CSV data in columnar chunks, similar to how parquet stores columns."""
csv_file = Path(csv_path)
with csv_f…Split CSV Files into Smaller Chunks in Python
Splits a large CSV file into multiple smaller chunk files, preserving the header row in each chunk.
import csv
import os
def split_csv(input_file, chunk_size=1000, output_prefix="chunk"):
"""Split a large CSV file into smaller chunks."""
with open(input_file, 'r', newline='') as infile:
reader = csv.reader(infile)
header = next(reader)
file_count = 1
row_count = 0
…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.
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)]
…How to partition a list into n nearly equal parts in Python
Divide a list into n contiguous chunks of nearly equal size using an average-length calculation that distributes the remainder evenly.
def partition(lst, n):
"""Partition a list into n nearly equal contiguous parts."""
if n <= 0:
raise ValueError("n must be positive")
if not lst:
return [[] for _ in range(n)]
parts = []
avg = len(lst) / n
last_idx = 0.0
while last_idx < len(lst):
end_idx =…Batch Rows in Chunks with a Generator in Python
Group a list of row dicts into fixed-size chunks using a generator that yields one slice per call.
from typing import Iterator, List
def batch_rows(rows: List[dict], batch_size: int) -> Iterator[List[dict]]:
for i in range(0, len(rows), batch_size):
yield rows[i:i + batch_size]
if __name__ == "__main__":
sample_rows = [
{"id": 1, "name": "Alice"},
{"id": 2, "name": "Bob"},
…How to Split Data into Chunks and Use Generators in Python
Split a list into fixed-size chunks with a list comprehension and square even numbers lazily with a generator expression.
def split_numbers(data, chunk_size):
return [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)]
def square_even_numbers(numbers):
return (n ** 2 for n in numbers if n % 2 == 0)
if __name__ == "__main__":
sample_data = list(range(1, 21))
chunks = split_numbers(sample_data, 5)
print…How to stream parse JSON arrays in Python
This code demonstrates two generators: one that streams a JSON array as individual chunks, and another that incrementally parses those chunks into Python objects using json.JSONDecoder.
import json
def json_array_stream(items):
"""Generator that yields JSON-encoded values one at a time."""
yield "["
for i, item in enumerate(items):
if i > 0:
yield ","
yield json.dumps(item)
yield "]"
def parse_json_stream(stream):
"""Consumes a stream of JSON fragme…Cosine Similarity to Retrieve Top K Chunks in Python
Compute cosine similarity between a query vector and a list of chunk vectors, then return the indices and scores of the top k most similar chunks.
import numpy as np
from numpy.linalg import norm
def cosine_similarity(vec1, vec2):
return np.dot(vec1, vec2) / (norm(vec1) * norm(vec2))
def retrieve_top_k(query_vec, chunk_vectors, k=3):
similarities = [cosine_similarity(query_vec, vec) for vec in chunk_vectors]
top_indices = sorted(range(len(similarit…How to Chunk a Long Document for RAG Retrieval in Python
Split text into overlapping chunks at sentence boundaries using a custom Python function suitable for RAG retrieval pipelines.
import re
from pathlib import Path
def chunk_document(text, chunk_size=500, overlap=100):
"""Split text into overlapping chunks suitable for RAG retrieval."""
# Normalize whitespace
text = re.sub(r'\s+', ' ', text).strip()
chunks = []
start = 0
while start < len(text):
end = min(s…Benchmark Disk Write Speed in Python with tempfile
Benchmark raw disk write performance by writing a temporary file in 1MB chunks and measuring throughput in MB/s.
import os
import tempfile
import time
def benchmark_write(size_mb=50):
size_bytes = size_mb * 1024 * 1024
chunk = b'x' * 1024 * 1024 # 1 MB chunk
with tempfile.NamedTemporaryFile(delete=True) as tmp:
start = time.perf_counter()
written = 0
while written < size_bytes:
…How to Reduce Aggregate Counts from Mapped Chunks in Python
Combine a list of mapped chunk dictionaries into a single aggregated count dictionary using functools.reduce.
from functools import reduce
from collections import defaultdict
def aggregate_chunks(mapped_chunks):
"""Combine mapped chunk counts into a single aggregate dict."""
return reduce(
lambda acc, chunk: {
**acc,
**{k: acc.get(k, 0) + v for k, v in chunk.items()}
},
…Map Partition Over Chunks in Python with Multiprocessing and Mock
Process data in chunks across multiple CPU cores using multiprocessing Pool.map, and mock the chunk function to test partitioning behavior without heavy computation.
from multiprocessing import Pool
from unittest.mock import patch, Mock
def process_chunk(chunk):
return [x * x for x in chunk]
def map_partition_over_chunks(data, chunk_size, process_func=process_chunk):
chunks = [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)]
with Pool() as pool:
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