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How to Swap Two Indices in a Python List
Swap two elements at given indices in a Python list using simultaneous assignment, then return the modified list.
def swap_indices(lst, i, j):
lst[i], lst[j] = lst[j], lst[i]
return lst
if __name__ == "__main__":
my_list = [10, 20, 30, 40, 50]
print("Original list:", my_list)
swapped = swap_indices(my_list, 1, 3)
print("After swapping indices 1 and 3:", swapped)
How to Do Random Assignment in Python for A/B Tests
Assign each item to a binary group (0 or 1) with uniform probability using a small reusable function, optionally weighted, for A/B testing mocks.
import random
def random_assignment_uniform_mock(items, weights=None):
"""Assign each item to a group (0 or 1) with uniform probability."""
if weights is None:
# Default: each item independently gets 0 or 1 with 50% probability
return [random.randint(0, 1) for _ in items]
# Optional weight…
How to Mock Stratified Assignment by Segment in Python
Simulate stratified assignment for A/B experiments by sampling a fixed proportion of units from each segment, with deterministic seeds for reproducibility.
import random
def stratified_assignment(segments, seed=None):
"""
Mock stratified assignment: given a dict of segment -> population size,
return a dict of segment -> sampled unit ids (deterministic with seed).
"""
if seed is not None:
random.seed(seed)
rng = random.Random(seed)
res…
How to hash user IDs to experiment buckets in Python
Deterministically map a user ID to an experiment bucket using MD5 hashing, ensuring stable and consistent assignment for A/B testing.
import hashlib
def hash_user_to_bucket(user_id: str, num_buckets: int = 10) -> int:
"""Deterministically map a user ID to an experiment bucket (0..num_buckets-1)."""
digest = hashlib.md5(user_id.encode("utf-8")).hexdigest()
return int(digest, 16) % num_buckets
if __name__ == "__main__":
mock_users …
Rebalance Shard Ranges Across Nodes in Python
A mock rebalancing function that shuffles shard ranges and distributes them evenly across nodes using round-robin assignment.
import random
from dataclasses import dataclass
@dataclass
class Shard:
id: int
start: int
end: int
def rebalance_shards(shards: list[Shard], node_count: int) -> dict[int, list[Shard]]:
"""Mock rebalancing of shard ranges across nodes."""
all_ranges = [(s.start, s.end) for s in shards]
random…
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