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Set Matrix Zeroes in Python: Markers List Grid Demo
Given a matrix, this code finds all rows and columns that contain a zero and sets every element in those rows and columns to zero, using boolean marker arrays.
def set_zeroes(matrix):
rows, cols = len(matrix), len(matrix[0])
row_markers = [False] * rows
col_markers = [False] * cols
# First pass: record which rows and columns contain zeros
for i in range(rows):
for j in range(cols):
if matrix[i][j] == 0:
row_markers[i] …How to Download All Assets from GitHub Releases in Python
Downloads every asset attached to the latest GitHub release of a repository, saving them locally using the GitHub API and Python's requests and pathlib libraries.
import requests
import os
import zipfile
from pathlib import Path
def download_github_release_assets(owner: str, repo: str, output_dir: str = "release_assets") -> None:
"""Downloads all assets from the latest release of a GitHub repository."""
releases_url = f"https://api.github.com/repos/{owner}/{repo}/relea…Deduplicate events by ID within a window in Python
Deduplicate event streams by ID within sliding time windows, keeping the newest occurrence per window using heaps and sets.
import heapq
from collections import defaultdict
def deduplicate_events(events, window_size):
"""Return events deduplicated by id, keeping newest within each sliding window."""
# Index events by (timestamp, id) for deterministic ordering
events_by_id = defaultdict(list)
for ts, eid, *payload in events…How to Find Missing Values in Large Datasets in Python
Analyze missing values across multiple large pandas DataFrames with counts and percentages.
import pandas as pd
import numpy as np
def find_missing_values_summary(datasets):
"""Analyze missing values across multiple datasets (dict of name: DataFrame)."""
summary = {}
for name, df in datasets.items():
missing_count = df.isnull().sum()
total_rows = len(df)
missing_pct = (mi…How to create a dated snapshot path for a dataset in Python
Generate a versioned directory path combining a base directory, dataset name, and today's date, ready for creating snapshots in data pipelines.
import datetime
import os
from pathlib import Path
def snapshot_path(base_dir: str, dataset_name: str) -> Path:
"""Return a dated snapshot path for a dataset under a base directory."""
today = datetime.date.today().isoformat()
return Path(base_dir) / dataset_name / today
if __name__ == "__main__":
…Idempotent Pipeline Dedupe by Record ID Set in Python
Filters records against a persistent set of seen IDs, returning only new ones and the updated set for idempotent pipeline processing.
def dedupe_records(records, seen_ids=None):
"""Return records whose id has not been seen before."""
if seen_ids is None:
seen_ids = set()
unique = []
for record in records:
record_id = record.get("id")
if record_id not in seen_ids:
seen_ids.add(record_id)
…Upload Assets to GitHub Release with Python Mock
Simulates uploading binary and text assets to a GitHub release using a mock server, returning structured metadata for each upload.
import json
import os
import tempfile
from datetime import datetime
class ReleaseUploader:
"""Simulates uploading assets to a release with a mock server."""
def __init__(self, owner: str, repo: str, tag: str):
self.owner = owner
self.repo = repo
self.tag = tag
self.uploade…Mock Route53 change_resource_record_sets in Python
This code demonstrates how to mock AWS Route53 change_resource_record_sets API calls using the botocore Stubber, allowing you to test DNS update logic without touching real infrastructure.
import boto3
from botocore.exceptions import ClientError
def mock_change_resource_record_sets():
"""Demonstrates Route53 change_resource_record_sets with a mock client."""
# Create a mock Route53 client
route53 = boto3.client('route53', region_name='us-east-1',
aws_access_key_id…How to Parametrize Tests in Python with pytest
This code demonstrates how to use pytest's @pytest.mark.parametrize decorator to run a single test function against multiple input sets, ensuring comprehensive coverage with minimal code duplication.
import pytest
def multiply(a, b):
return a * b
@pytest.mark.parametrize("x, y, expected", [
(2, 3, 6),
(4, 5, 20),
(0, 10, 0),
(7, 1, 7),
])
def test_multiply(x, y, expected):
result = multiply(x, y)
assert result == expected, f"multiply({x}, {y}) = {result}, expected {expected}"
if _…How to Implement Sparse Fieldsets in Python
A function that filters API responses by resource type, returning only requested fields plus IDs, as a sparse fieldset mock.
from dataclasses import dataclass, field
from typing import Dict, List, Optional
@dataclass
class MockResponse:
data: Dict[str, object] = field(default_factory=dict)
included: List[Dict[str, object]] = field(default_factory=list)
def select_fields(
data: Dict[str, object],
sparse_fields: Optional[D…Kafka Consumer Poll Loop Mock in Python
Simulate a Kafka consumer poll loop with a mock class, process messages in batches, and commit offsets to understand streaming consumption patterns.
import time
class MockKafkaConsumer:
def __init__(self, topic, messages):
self.topic = topic
self.messages = list(messages)
self.position = 0
def poll(self, timeout_ms=100):
if self.position >= len(self.messages):
time.sleep(timeout_ms / 1000)
return []…How to Build a Redis Leaderboard with ZREVRANGE in Python
Build a sorted leaderboard by storing player scores as a Redis sorted set and reading the top scores with ZREVRANGE in Python.
import redis
import random
# Connect to local Redis (ensure Redis is running on localhost:6379)
r = redis.Redis(host="localhost", port=6379, db=0, decode_responses=True)
# Clear any existing test data
r.delete("game_scores")
# Simulate player scores
players = ["alice", "bob", "charlie", "dave", "eve"]
for player in…Redis SADD SMEMBERS Set Mock in Python
A lightweight mock of Redis SADD and SMEMBERS using Python sets for testing or local caching.
class RedisSetMock:
def __init__(self):
self.sets = {}
def sadd(self, key, *members):
if key not in self.sets:
self.sets[key] = set()
before = len(self.sets[key])
self.sets[key].update(members)
return len(self.sets[key]) - before
def smembers(self, key)…Exactly Once Processing Dedupe Mock in Python
Implements a streaming deduplicator using a set and queue to guarantee each item is processed exactly once while preserving insertion order.
from collections import deque
class DedupeStream:
def __init__(self):
self.seen = set()
self.queue = deque()
def add(self, item):
if item not in self.seen:
self.seen.add(item)
self.queue.append(item)
print(f"Processed: {item} (exactly once)")
…How to Mock Daily and Monthly Quota Counters in Python
Track daily and monthly API call usage with automatic resets, quota checks, and limits using a Python class.
import random
from datetime import datetime, timedelta
class QuotaCounter:
def __init__(self, daily_limit=1000, monthly_limit=20000):
self.daily_limit = daily_limit
self.monthly_limit = monthly_limit
self.daily_usage = 0
self.monthly_usage = 0
self.current_day = datetime.n…How to Define Dagster ML Assets in Python
Define a chain of Dagster software-defined assets that compute raw features, normalized features, and predictions for an ML pipeline.
from dagster import asset
@asset
def raw_features():
return {"sepal_length": [5.1, 4.9, 6.2], "sepal_width": [3.5, 3.0, 3.4]}
@asset
def normalized_features(raw_features):
values = raw_features["sepal_length"]
mean = sum(values) / len(values)
std = (sum((x - mean) ** 2 for x in values) / len(values…Approximate Count with HyperLogLog in Python
A mock HyperLogLog implementation uses hash-based registers to estimate cardinality of large datasets with sublinear memory.
import hashlib
class HyperLogLog:
def __init__(self, precision=4):
if precision < 4 or precision > 16:
raise ValueError("precision must be between 4 and 16")
self.precision = precision
self.registers = [0] * (1 << precision)
def _hash(self, value):
return int(hashl…Browse by section
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