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How to Mock DataFrame Schema Columns in Python
Create an empty pandas DataFrame with only the specified column names to mock a schema before any data is loaded.
import pandas as pd
def mock_schema(columns):
return pd.DataFrame(columns=columns)
if __name__ == "__main__":
cols = ["name", "age", "city"]
df = mock_schema(cols)
print(df)
print(f"Columns: {list(df.columns)}, Shape: {df.shape}")How to Build a Simple ML Pipeline with ZenML in Python
Build a mock machine learning pipeline with ZenML steps for data loading, training, and evaluation, and run it to print the final accuracy.
from zenml import pipeline, step
@step
def load_data() -> dict:
"""Simulate loading data from a source."""
return {"accuracy": 0.0, "loss": 1.0}
@step
def train_model(data: dict) -> dict:
"""Simulate training a model."""
data["accuracy"] = 0.95
data["loss"] = 0.1
return data
@step
def eva…How to Load CSV Training Data in Python Without Pandas
Load CSV training data using Python's standard library and mock it with io.StringIO for testing, returning headers and rows as dictionaries.
import csv
from pathlib import Path
def load_csv_training_data(file_path: str | Path) -> tuple[list[str], list[dict[str, str]]]:
"""Load CSV training data and return headers plus rows as dictionaries."""
with open(file_path, mode="r", newline="", encoding="utf-8") as csv_file:
reader = csv.DictReader…How to Load, Save, and Split JSON Data in Python
Provides helper functions to load, save, and split JSON dictionary data for simple ML pipeline preprocessing.
import json
from pathlib import Path
def load_json_data(file_path):
"""Load JSON data from a file, returning an empty dict if missing."""
path = Path(file_path)
if path.exists():
with path.open("r", encoding="utf-8") as f:
return json.load(f)
return {}
def save_json_data(data, f…How to Save and Load PyTorch Model State Dict in Python
This code demonstrates how to save a PyTorch model's state dict to a file and load it back into a new model instance, verifying weights match.
import torch
import torch.nn as nn
class SimpleNet(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 8)
self.fc2 = nn.Linear(8, 2)
def forward(self, x):
x = torch.relu(self.fc1(x))
return self.fc2(x)
if __name__ == "__main__":
model = Simp…How to Save and Load a Mock Model with Pickle and joblib in Python
Serialize a custom machine learning model to a .joblib file with joblib.dump, reload it, and run a prediction with joblib.load.
import joblib
from pathlib import Path
class MockModel:
def __init__(self, weights):
self.weights = weights
def predict(self, features):
return sum(w * f for w, f in zip(self.weights, features))
def save_model_pickle(model, filepath):
with open(filepath, "wb") as f:
joblib.dump(…Load CSV Training Data Without Pandas in Python
This code loads a CSV file into a list of dictionaries using only the standard library, ideal for small ML training data without heavy dependencies.
import csv
from pathlib import Path
def load_csv(path):
"""Load CSV file into list of dicts without pandas."""
rows = []
with open(path, newline='', encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
rows.append(dict(row))
return rows
if __name__ == "__m…Broadcast a Small Reference Table in Python
Simulates SQL-style broadcasting of a small lookup table against a larger fact table in memory for mockups or load tests.
import random
def broadcast_mock(target, source, columns):
result = {}
for col in columns:
if col in target and col in source:
result[col] = target[col] + [source[col][i % len(source[col])] for i in range(len(target[col]))]
elif col in target:
result[col] = target[col]
…How to Batch Load JSON Data in Python for Database Optimization
This code parses JSON data into records and loads them in batches to simulate efficient database insertion, reducing load and improving performance.
import json
import time
def parse_and_load(data, batch_size=100):
"""
Parse JSON data and batch-load into a list of dicts.
Demonstrates batching for database efficiency.
"""
records = json.loads(data)
batches = []
for i in range(0, len(records), batch_size):
batch = records[i:i + …Route SELECT Queries to Read Replicas in Python
A mock round-robin router that forwards SELECT queries to read replicas and sends writes to the primary.
import random
class ReadReplicaRouter:
"""Round-robin router that sends SELECT queries to read replicas."""
def __init__(self, replicas):
self.replicas = replicas
self.counter = 0
def route(self, sql):
if sql.strip().upper().startswith("SELECT"):
replica = sel…Build a Mock OIDC Userinfo Endpoint in Python with Flask
Create a local mock OIDC userinfo endpoint in Flask that returns a standard JSON user payload, ideal for testing auth flows without a real identity provider.
from flask import Flask, jsonify
app = Flask(__name__)
@app.route("/userinfo")
def userinfo():
mock_user = {
"sub": "1234567890",
"name": "John Doe",
"email": "john@example.com",
"email_verified": True,
"groups": ["admin", "dev"]
}
return jsonify(mock_user)
if __n…How to Implement an HSTS Preload List Mock in Python
Implements a mock HSTS preload list in Python that supports adding, removing, checking domains with subdomain inheritance, and listing domains.
import json
class HSTSPreloadList:
def __init__(self):
self.domains = {}
def add_domain(self, domain, include_subdomains=False, max_age=31536000):
self.domains[domain] = {
"include_subdomains": include_subdomains,
"max_age": max_age
}
def remove_domain(sel…Design a Data Helper for Beginners in Python
Build a beginner-friendly DataHelper class that loads, saves, appends, and summarizes JSON data with atomic file writes.
import json
from datetime import datetime
from pathlib import Path
class DataHelper:
"""A beginner-friendly helper for common data operations."""
def __init__(self, data=None, filepath=None):
self.data = data if data is not None else []
self.filepath = Path(filepath) if filepath else None
…How to Attach an SBOM to a Release in Python (Mock)
A mock function that attaches a Software Bill of Materials (SBOM) to a GitHub-style release by counting its components and marking the upload as attached.
import json
from pathlib import Path
def attach_sbom_mock(sbom_path: Path, release_tag: str, artifact_name: str) -> dict:
"""Mock attaching an SBOM to a release, returning the simulated upload result."""
sbom = json.loads(sbom_path.read_text())
return {
"release_tag": release_tag,
"artifa…How to Build a Data Helper for Production Deployment in Python
Build a reusable DataHelper class that loads configs, validates required keys, normalizes string values, and logs schema details — a production-ready data processing pattern.
import json
from pathlib import Path
from typing import Any, Dict
class DataHelper:
"""Common data processing patterns for production deployment."""
def __init__(self, config_path: str | Path):
self.config_path = Path(config_path)
self.config = self._load_config()
def _load_confi…How to Build a Simple Data Helper Class in Python
A beginner-friendly DataHelper class that safely saves and loads JSON files with automatic directory creation, perfect for production-style file handling.
from pathlib import Path
import json
class DataHelper:
"""Simple production-style helper for loading and saving JSON data."""
def __init__(self, data_dir="data"):
self.data_dir = Path(data_dir)
self.data_dir.mkdir(exist_ok=True)
def save(self, filename, data):
filepath = self.da…How to Build a Simple Data Helper Class in Python
A beginner-friendly DataHelper class that stores Python dataclass objects as JSON records to disk, with load, add, and save methods.
import json
from dataclasses import dataclass, asdict
from pathlib import Path
@dataclass
class User:
name: str
age: int
email: str
class DataHelper:
def __init__(self, filepath: str = "data.json"):
self.filepath = Path(filepath)
self._data = self._load()
def _load(self) -> l…How to Implement a Data Helper Class in Python for Production Deployments
Build an environment-aware data helper in Python that loads config, extracts, transforms, and reports on JSON data using small, testable functions.
"""Production-style data helper for beginners.
Demonstrates:
- environment-aware config
- central data extraction
- small, testable functions
"""
import os
import json
from pathlib import Path
from typing import List, Dict, Any
def load_config(env: str = os.getenv("APP_ENV", "development")) -> Dict[str, Any]:
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