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How to Sample Random Items Without Replacement in Python
Select k random unique items from a sequence using random.sample for uniform, non-repeating selection.
import random
def sample_without_replacement(population, k):
"""Return k random items from population without replacement."""
if k > len(population):
raise ValueError("k cannot exceed population size")
# Use random.sample for O(k) time, no mutation of the original
return random.sample(populati…How to Build a Weighted Random Load Balancer in Python
A Python load balancer mock that distributes requests across servers based on configurable weights using a cumulative weighted random selection algorithm.
import random
from collections import Counter
SERVERS = {
"server-a": 50,
"server-b": 30,
"server-c": 20,
}
def weighted_random_server(servers: dict[str, int]) -> str:
"""Select a server based on its weight (higher weight = more likely)."""
total_weight = sum(servers.values())
rand = random.…Mock NATS queue group load balancing in Python
Simulates a NATS queue group where each message is delivered to exactly one subscriber using random selection with a lightweight mock.
import random
import time
from collections import defaultdict
class MockQueueGroup:
"""Mock a NATS queue group: each message is delivered to exactly one subscriber."""
def __init__(self, subscribers):
self.subscribers = subscribers
def publish(self, message):
receiver = random.choice(se…How to Mock a Server-Side Load Balancer in Python
A simple Python class that mimics a server-side load balancer with round-robin, random, and least-connections selection strategies.
import itertools
import random
class LoadBalancer:
def __init__(self, servers=None):
self.servers = servers if servers else ["server1", "server2", "server3"]
self.counter = itertools.count(1)
def round_robin(self):
return next(self.counter) % len(self.servers)
def random_selectio…How to do feature selection with VarianceThreshold in Python
This code demonstrates how to use scikit-learn's VarianceThreshold to remove low-variance features from a NumPy array, keeping only those that vary enough to be useful for modeling.
import numpy as np
from sklearn.feature_selection import VarianceThreshold
def main():
# Mock dataset: 4 samples, 5 features
X = np.array([
[0.1, 0.2, 1.0, 1.0, 0.5],
[0.2, 0.2, 0.0, 1.0, 0.4],
[0.1, 0.2, 1.0, 1.0, 0.6],
[0.3, 0.2, 1.0, 0.0, 0.5]
])
# Select features w…Browse by section
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Guide: free Python code samples library
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Samples vs tutorials and challenges
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