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Python Code Samples

Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.

52 matches
ML engineering pipelines medium

How to mock an artifact store with local paths in Python for ML pipelines

Create a temporary local artifact store with dummy files and metadata to test ML pipeline code without real storage.

ml-pipelines mock tempfile
Python
import tempfile
from pathlib import Path
import json


def create_artifact_store_mock(base_path: Path = None):
    """Create a local artifact store mock directory structure."""
    if base_path is None:
        base_path = Path(tempfile.mkdtemp())

    store_layout = {
        "artifacts": [
            {"name": "mode…
13 0 Open
A/B testing & experimentation easy

How to Calculate Weighted Grades and Generate Mock Notes in Python

Compute a weighted physics grade from exam and homework scores, then generate a performance-based mock note with percentage and feedback.

grades weighted-average mock-note
Python
def get_physics_grade(exam_score, homework_score):
    """Calculate final grade from exam and homework scores."""
    exam_weight = 0.7
    homework_weight = 0.3
    return (exam_score * exam_weight) + (homework_score * homework_weight)


def mock_note(correct_score, max_score, student_name):
    """Generate a mock no…
10 0 Open
Database scaling & optimization easy

How to Convert Data with Scaling for Database Optimization in Python

A beginner-friendly helper that normalizes and scales numeric fields in a list of dicts, reducing storage footprint for database efficiency.

data conversion database scaling
Python
import json
from datetime import datetime

def convert_data(data: list[dict], scale_factor: int = 1) -> list[dict]:
    """Convert a list of dicts to a scaled, normalized format for database efficiency."""
    converted = []
    for row in data:
        normalized = {}
        for key, value in row.items():
          …
14 0 Open
Production deployment patterns easy

How to Build a Synthetic Monitor Mock in Python

Simulates a synthetic monitoring system in Python that collects latency samples, averages them, and reports service status as UP or DEGRADED.

monitoring dataclass simulation
Python
import random
import time
from dataclasses import dataclass, field
from statistics import mean


@dataclass
class SyntheticMonitor:
    service: str
    endpoint: str
    latency_ms: list[float] = field(default_factory=list)

    def check(self) -> float:
        latency = random.uniform(50.0, 250.0)
        self.late…
13 0 Open

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