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Python Code Samples

Easy snippets you can copy, study, and run in the browser editor.

24 matches
Lists & loops easy

How to Compute Sliding Window Sum of Size k in Python

Compute the sum of every contiguous subarray of a fixed size k using an efficient O(n) sliding window technique.

sliding-window list sum
Python
def sliding_window_sum(nums, k):
    """Return a list of sums for each contiguous subarray of size k."""
    if not nums or k <= 0 or k > len(nums):
        return []
    
    result = []
    window_sum = sum(nums[:k])
    result.append(window_sum)
    
    for i in range(k, len(nums)):
        window_sum += nums[i] -…
12 0 Open
Lists & loops easy

How to Compute a Moving Average in Python

This code computes the moving average over a numeric list using an efficient sliding window sum, avoiding recomputation of each window.

moving-average sliding-window lists
Python
def moving_average(data, window_size):
    """
    Compute the moving average over a numeric list.
    
    Args:
        data: List of numeric values
        window_size: Size of the sliding window (positive integer)
    
    Returns:
        List of moving averages, each representing the mean of a window
    """
   …
14 0 Open
Algorithms & data structures easy

How to Apply a Function to Sliding Window Slices in Python

This Python code applies a given function to every contiguous window of a specified size in a list, returning a list of results.

sliding-window list-comprehension algorithms
Python
def apply_to_sliding_windows(data, window_size, func):
    return [func(data[i:i + window_size]) for i in range(len(data) - window_size + 1)]

if __name__ == "__main__":
    numbers = [1, 2, 3, 4, 5, 6]
    window_size = 3
    results = apply_to_sliding_windows(numbers, window_size, sum)
    print(results)
    results…
16 0 Open
Algorithms & data structures easy

How to Implement a Moving Average from a Data Stream in Python

Implement a MovingAverage class using a deque and running sum to compute the average of the last k values from a continuous data stream.

deque sliding-window streaming
Python
from collections import deque

class MovingAverage:
    def __init__(self, size):
        self.size = size
        self.queue = deque()
        self.window_sum = 0

    def next(self, val):
        self.queue.append(val)
        self.window_sum += val

        if len(self.queue) > self.size:
            self.window_su…
12 0 Open
Algorithms & data structures easy

How to Implement a Recent Counter with a Deque in Python

Implements a RecentCounter class that uses a deque to count ping requests within the last 3000 milliseconds.

deque recents sliding-window
Python
from collections import deque
import time


class RecentCounter:
    def __init__(self):
        self.hits = deque()

    def ping(self, t: int) -> int:
        self.hits.append(t)
        while self.hits and self.hits[0] < t - 3000:
            self.hits.popleft()
        return len(self.hits)


if __name__ == "__mai…
11 0 Open
Comprehensions & generators easy

How to Build a Sliding Window Generator in Python

Create a generator that yields fixed-size overlapping slices of a sequence, useful for efficient windowed iteration.

generators sliding-window iteration
Python
def sliding_window(sequence, size):
    for i in range(len(sequence) - size + 1):
        yield sequence[i:i + size]

if __name__ == "__main__":
    data = [1, 2, 3, 4, 5]
    n = 3
    for window in sliding_window(data, n):
        print(window)
12 0 Open
Data pipelines & processing easy

How to Implement a Sliding Window Average in Python

Compute the average of the most recent N values in a stream using a bounded deque, efficiently updating the total as new values arrive.

deque sliding-window streaming
Python
from collections import deque


class SlidingWindowAverage:
    def __init__(self, window_size):
        self.window_size = window_size
        self.window = deque(maxlen=window_size)
        self.total = 0

    def add(self, value):
        if len(self.window) == self.window_size:
            self.total -= self.windo…
15 0 Open
Streaming & messaging easy

How to Implement a Tumbling Window Counter in Python

Count events that fall within a fixed-size sliding time window using a deque and pruning logic.

streaming window aggregation
Python
from collections import deque
import time


class TumblingWindowCounter:
    def __init__(self, window_size_seconds):
        self.window_size = window_size_seconds
        self.window = deque()

    def add_event(self, timestamp):
        self.window.append(timestamp)

    def count(self, current_time):
        while…
14 0 Open
Streaming & messaging easy

Sliding Window Average with Deque in Python

Computes the running average of a sliding window over streaming numbers using a collections.deque for O(1) pop-left operations.

sliding-window deque streaming
Python
from collections import deque

class SlidingAverage:
    def __init__(self, window_size):
        self.window_size = window_size
        self.window = deque()
        self.total = 0

    def add(self, value):
        self.window.append(value)
        self.total += value
        if len(self.window) > self.window_size:
…
13 0 Open
Reliability & rate limiting easy

Build a Rate Limiter Decorator in Python

This code defines a reusable rate limiter decorator that caps function calls within a sliding time window using a deque and monotonic time.

rate-limiting decorator time
Python
import time
from collections import deque


def rate_limiter(max_calls: int, period: float):
    calls = deque()

    def decorator(func):
        def wrapper(*args, **kwargs):
            now = time.monotonic()
            while calls and now - calls[0] >= period:
                calls.popleft()
            if len(ca…
13 0 Open
Reliability & rate limiting easy

Fixed Window Counter Rate Limiting in Python

A simple fixed window counter rate limiter that allows a maximum number of requests per 60-second window, with a mock time simulation.

rate-limiting fixed-window time
Python
from collections import deque
from time import time

class FixedWindowCounter:
    def __init__(self, max_requests):
        self.max_requests = max_requests
        self.window_start = int(time())
        self.window_count = 0

    def allow_request(self):
        current_time = int(time())
        if current_time >=…
13 0 Open
Reliability & rate limiting easy

How to Build a Rate Limiter in Python

Implements a simple sliding-window rate limiter that caps the number of calls per period, used to throttle processing of a data list.

rate-limiting time sliding-window
Python
import time

class RateLimiter:
    def __init__(self, max_calls, period):
        self.max_calls = max_calls
        self.period = period
        self.timestamps = []

    def allow(self):
        now = time.time()
        self.timestamps = [t for t in self.timestamps if now - t < self.period]
        if len(self.tim…
14 0 Open
Reliability & rate limiting easy

How to Implement a Sliding Window Counter in Python

This code implements an approximate sliding window counter using a deque of time-based buckets to track event counts within a recent time window.

sliding-window rate-limiting deque
Python
from collections import deque
from time import time


class SlidingWindowCounter:
    def __init__(self, window_size, bucket_size=1):
        self.window_size = window_size
        self.bucket_size = bucket_size
        self.buckets = deque()

    def _evict_expired(self, now):
        while self.buckets and self.buck…
13 0 Open
Reliability & rate limiting easy

How to implement rate limiting in Python

Build a simple sliding-window rate limiter in Python that enforces a max number of calls per time period and formats data with timestamps.

rate-limiting time sliding-window
Python
import time

class RateLimiter:
    def __init__(self, max_calls, period):
        self.max_calls = max_calls
        self.period = period
        self.calls = []
    
    def allow(self):
        now = time.time()
        # Remove calls older than the period window
        self.calls = [t for t in self.calls if now -…
17 0 Open
Reliability & rate limiting easy

Rate Limit per User ID in Python with a Dict Mock

Implements a simple sliding window rate limiter using a defaultdict of timestamps per user ID, blocking requests that exceed a max count within a time window.

rate-limiting defaultdict sliding-window
Python
import time
from collections import defaultdict


class RateLimiter:
    def __init__(self, max_requests, window_seconds):
        self.max_requests = max_requests
        self.window_seconds = window_seconds
        self.user_timestamps = defaultdict(list)

    def allow_request(self, user_id):
        now = time.tim…
14 0 Open
Reliability & rate limiting easy

Rate Limiting in Python with a Sliding Window

A beginner-friendly dataclass-based sliding window rate limiter that controls how many calls are allowed per time window.

rate-limiting sliding-window dataclass
Python
import time
from dataclasses import dataclass


@dataclass
class RateLimiter:
    max_calls: int
    window_seconds: float = 1.0

    def __post_init__(self):
        self.calls = []
        self._start = time.monotonic()

    def _update(self, now):
        self.calls = [t for t in self.calls if now - t < self.window…
12 0 Open
Reliability & rate limiting easy

Rate Limiting with a Simple Python RateLimiter Class

A beginner-friendly Python rate limiter that tracks call timestamps and enforces a maximum number of calls within a rolling time window, with a helper to validate positive integers.

rate-limiting time api
Python
import time

class RateLimiter:
    def __init__(self, max_calls, period_seconds):
        self.max_calls = max_calls
        self.period_seconds = period_seconds
        self.calls = []

    def is_allowed(self):
        now = time.time()
        while self.calls and now - self.calls[0] >= self.period_seconds:
      …
13 0 Open
Observability & SRE easy

Calculate Error Rate from Log Stream in Python

Parses a mock log stream to count errors and compute the error percentage using a rolling window of recent entries.

logging regex error-rate
Python
import re
from collections import deque

def error_rate_from_log_stream(message):
    log_pattern = r'^\[(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2})\] (ERROR|INFO|DEBUG): (.*)$'
    recent_entries = deque(maxlen=100)
    error_count = 0
    total_count = 0

    for line in message.strip().split('\n'):
        match = re.mat…
15 0 Open
Observability & SRE easy

Check if a Timestamp Falls in a Daily Maintenance Window in Python

A small Python function that returns True when a datetime falls inside a daily maintenance window, and a demo printing yes/no for sample timestamps.

maintenance datetime scheduling
Python
from datetime import datetime, timedelta
from zoneinfo import ZoneInfo


def in_maintenance_window(now: datetime, start_hour: int = 2, duration_hours: int = 4) -> bool:
    """Return True if 'now' falls inside the daily maintenance window."""
    day_start = now.replace(hour=start_hour, minute=0, second=0, microsecond…
15 0 Open
Observability & SRE easy

How to Compute SRE Metrics Like Error Rate and Availability in Python

Tracks log events in a sliding time window and calculates error rate per second and availability percentage using an easy-to-follow class.

observability sre metrics
Python
from collections import deque
from datetime import datetime, timedelta
from typing import Dict, Deque


class LogMetrics:
    """Simple observability helper to track log events and calculate SRE metrics."""

    def __init__(self, window_seconds: int = 60):
        self.window_seconds = window_seconds
        self.eve…
14 0 Open
Observability & SRE easy

How to mock Prometheus alert rule thresholds in Python

Simulate a Prometheus alert rule with a configurable threshold and duration window, firing only when the metric exceeds the threshold long enough.

prometheus alerting sre
Python
import time
import random


class MetricsStore:
    def __init__(self):
        self.metrics = {}

    def set_metric(self, name, value, labels=None):
        key = (name, tuple(sorted((labels or {}).items())))
        self.metrics[key] = value

    def get_metric(self, name, labels=None):
        key = (name, tuple(s…
14 0 Open
Big data & Spark easy

Session window gap mock in Python

Group sorted timestamps into sessions where any gap between consecutive events exceeds a threshold starts a new session.

timestamps sessions windowing
Python
from datetime import datetime, timedelta


def session_windows(timestamps, gap_seconds=300):
    """Group timestamps into sessions where gaps > gap_seconds start new sessions."""
    if not timestamps:
        return []

    # Sort timestamps chronologically to ensure correct windowing
    timestamps = sorted(timestam…
14 0 Open
Big data & Spark easy

Sliding Window Streaming Mock in Python

A simple Python class that maintains a sliding window of recent streaming values and computes the running average.

streaming sliding-window averages
Python
import time
import random

class StreamingMock:
    """Produces a stream of numbers using a sliding window."""
    
    def __init__(self, window_size=5):
        self.window = []
        self.window_size = window_size
        
    def push(self, value):
        """Add a value, sliding the window forward."""
        s…
12 0 Open
Database scaling & optimization easy

How to Mock Date Sharding by Range in Python

Split a date interval into fixed-size contiguous shards, returning each window as an ISO date string pair.

date datetime sharding
Python
from datetime import date, timedelta

def shard_ranges(start_date, end_date, shard_days=7):
    if start_date > end_date:
        raise ValueError("start_date cannot be after end_date")

    shards = []
    current = start_date
    while current <= end_date:
        shard_end = min(current + timedelta(days=shard_days …
13 0 Open

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Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.