Files & data
Read and write files safely; parse JSON, CSV, and common text formats.
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…Download Files from Internet with Progress Bar in Python
Download a file from the internet while displaying a text progress bar in the terminal.
import urllib.request
import sys
def download_with_progress(url, filename):
"""Download a file with a simple text progress bar."""
def report_hook(block_count, block_size, total_size):
downloaded = block_count * block_size
if total_size > 0:
percent = min(100, int(downloaded * 100 …How to Load Pickle Files Safely in Python
This code demonstrates how to load pickle files safely in Python by using a restricted unpickler that only allows specific, trusted classes, preventing arbitrary code execution from untrusted pickles.
import pickle
# Default pickle.load is unsafe: it executes arbitrary code when unpickling.
class Unsafe:
def __reduce__(self):
return (eval, ("open('/tmp/pickle_demo.txt', 'w').write('pwned')",))
# Create a malicious payload (simulating untrusted source)
malicious_data = pickle.dumps(Unsafe())
# Safe ap…How to Memory Map Large Files Read-Only in Python
This code demonstrates reading only the tail of a large file using a read-only memory map (mmap) to avoid loading the entire file into memory.
import mmap
import os
def read_tail_with_mmap(filepath, bytes_from_end=64):
"""Read the last bytes of a large file using a read-only mmap."""
file_size = os.path.getsize(filepath)
start = max(0, file_size - bytes_from_end)
with open(filepath, "rb") as f:
with mmap.mmap(f.fileno(), length=0, a…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:
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