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Python Code Samples

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

3 matches
ML engineering pipelines easy

Build a Data Helper Class in Python for ML Pipelines

A beginner-friendly Python class that summarizes, filters, and exports ML dataset rows as JSON.

data-helper ml-pipeline json
Python
from typing import List, Dict, Any
import json

class DataHelper:
    """Beginner-friendly helpers for ML data pipelines."""
    
    def __init__(self, data: List[Dict[str, Any]]):
        self.data = data
        self.keys = list(data[0].keys()) if data else []
    
    def summary(self) -> Dict[str, Any]:
        "…
15 0 Open
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
ML engineering pipelines easy

One Hot Encode Categories in Python

Convert a list of categorical strings into one-hot encoded numeric vectors using pure Python and NumPy.

one-hot encoding categorical numpy
Python
import numpy as np

categories = ["red", "green", "blue", "red", "blue", "green", "red"]

unique = sorted(set(categories))
lookup = {cat: i for i, cat in enumerate(unique)}

one_hot = []
for cat in categories:
    row = [0] * len(unique)
    row[lookup[cat]] = 1
    one_hot.append(row)

print("Categories:", categories…
13 0 Open

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