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How to Render a Jinja-like Template from a Dict in Python
Replace {{placeholders}} in a string using values from a Python dict with a simple regex-based template renderer.
import re
def render_template(template, context):
pattern = re.compile(r"\{\{\s*(\w+)\s*\}\}")
def replace(match):
key = match.group(1)
return str(context.get(key, ""))
return pattern.sub(replace, template)
if __name__ == "__main__":
template = "Hello {{name}}, you have {{count}} new …How to Build a Simple Data Pipeline in Python
A beginner-friendly data pipeline that loads JSON, filters records by a field value, and aggregates counts per category.
import json
from pathlib import Path
def load_json(filepath: str | Path) -> list[dict]:
"""Load a JSON file containing a list of records."""
with Path(filepath).open("r", encoding="utf-8") as f:
return json.load(f)
def filter_records(records: list[dict], field: str, value) -> list[dict]:
"""Kee…How to Convert Data Types in a Python Data Pipeline
Demonstrates a simple Python data pipeline that converts string values to proper types (bool, int, float, datetime) and outputs structured JSON.
import json
from datetime import datetime
def convert_value(value):
"""Convert string values to appropriate Python types."""
if value.lower() == "true":
return True
if value.lower() == "false":
return False
if value.isdigit():
return int(value)
try:
return float(val…How to Explode an Array Field into Multiple Rows in Python
This code flattens a list of dictionaries by exploding each array field value into its own row, duplicating the other fields as needed.
from collections import defaultdict
data = [
{"id": 1, "name": "Alice", "tags": ["python", "data", "ai"]},
{"id": 2, "name": "Bob", "tags": ["web", "devops"]},
{"id": 3, "name": "Carol", "tags": []},
]
def explode_array_field(records, array_field):
result = []
for record in records:
for v…How to Filter Data in Python
Filter a list of dictionaries by exact key-value matches or numerical ranges using concise list comprehensions.
from typing import List, Dict, Any
def filter_data(
data: List[Dict[str, Any]], key: str, value: Any
) -> List[Dict[str, Any]]:
"""Return records where data[key] equals value."""
return [record for record in data if record.get(key) == value]
def filter_by_range(
data: List[Dict[str, Any]], key: str…How to Group Rows by Key into Nested Arrays in Python
This code groups rows in a list of dictionaries by a specified key and returns a dictionary with each key mapped to a list of values from another key.
from collections import defaultdict
def implode_rows(rows, key, value_key):
grouped = defaultdict(list)
for row in rows:
grouped[row[key]].append(row[value_key])
return dict(grouped)
if __name__ == "__main__":
data = [
{"category": "fruit", "item": "apple"},
{"category": "fr…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.
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…How to Merge Incremental Snapshot Upsert Dict in Python
Merge a snapshot dict into a base dict, recursively updating nested dictionaries while preferring snapshot values on conflicts.
def merge_upsert(base: dict, snapshot: dict) -> dict:
"""
Merge a snapshot dict into a base dict, preferring snapshot values
on key conflicts (upsert semantics). Nested dicts are merged recursively.
"""
result = dict(base)
for key, value in snapshot.items():
if key in result and i…How to Safely Coerce Strings to Numbers in Python
A safe conversion function that turns strings into integers or floats, returning a fallback value when conversion fails.
import math
def to_number(value, fallback=None):
"""Safely coerce a string to int or float, returning fallback on failure."""
if isinstance(value, (int, float)):
return value
try:
# Try int first for clean whole numbers
return int(value)
except (ValueError, TypeError):
…How to detect anomalies in a column using z-score in Python
Detect outliers in a list of numbers using z-score statistics, flagging values that deviate significantly from the mean.
import random
def z_score_anomaly_detection(data, threshold=2.0):
"""
Detect anomalies in a list of numbers using z-score.
"""
mean = sum(data) / len(data)
variance = sum((x - mean) ** 2 for x in data) / len(data)
std_dev = variance ** 0.5
if std_dev == 0:
return []
a…Test a Python Pipeline with Fixture Sample Rows
Test pipeline functions with sample rows provided by a pytest fixture, verifying required keys and value constraints.
import pytest
def get_value(data: dict, key: str):
return data.get(key)
def sample_rows():
return [
{"name": "Alice", "age": 30, "city": "London"},
{"name": "Bob", "age": 25, "city": "Paris"},
{"name": "Charlie", "age": 35, "city": "Berlin"},
]
@pytest.fixture
def sample_data(…Union Multiple DataFrames with Aligned Columns in Python
Concatenate DataFrames with different columns, aligning them and filling missing values with NaN using pandas concat.
import pandas as pd
from io import StringIO
# Sample dataframes with different columns
df1 = pd.DataFrame({
'id': [1, 2, 3],
'name': ['Alice', 'Bob', 'Charlie'],
'age': [25, 30, 35]
})
df2 = pd.DataFrame({
'id': [4, 5],
'name': ['Diana', 'Eve'],
'city': ['NYC', 'LA']
})
df3 = pd.DataFrame({
…How to Mock AWS Secrets Manager in Python
Create a lightweight mock of AWS Secrets Manager's get_secret_value API to test secret retrieval without cloud dependencies.
import json
from typing import Optional
class MockSecretsManager:
"""A simple mock of AWS Secrets Manager's get_secret_value API."""
def __init__(self):
self._secrets: dict[str, str] = {}
def create_secret(self, secret_id: str, secret_value: str) -> None:
"""Store a secret value under a…How to Mock DynamoDB with a Simple Dict Store in Python
A lightweight in-memory DynamoDB mock that stores items in a dict and supports put, get, and query-by-value operations for local testing.
import json
from typing import Any, Dict, Optional
class MockDynamoDB:
def __init__(self) -> None:
self._store: Dict[str, Dict[str, Any]] = {}
def put_item(self, table_name: str, item: Dict[str, Any]) -> None:
key = str(item.get("id"))
if table_name not in self._store:
se…How to Parse Terraform Output JSON in Python
Parse Terraform's JSON output into a flat dictionary of values using the standard library json module.
import json
def parse_terraform_output(raw_output):
"""Parse Terraform JSON output into a flat dict of values."""
try:
data = json.loads(raw_output)
except json.JSONDecodeError as e:
raise ValueError(f"Invalid JSON: {e}")
return {key: value["value"] for key, value in data.items()}
i…Pick a Random Region with Mock Carbon Intensity in Python
Selects a random region from a list and generates a mock carbon intensity value using Python's random module.
import random
def pick_region_intensity(regions, seed=42):
random.seed(seed)
selected = random.choice(regions)
intensity = random.randint(1, 10)
return selected, intensity
if __name__ == "__main__":
regions = ["North", "South", "East", "West"]
selected, intensity = pick_region_intensity(regio…How to Parse and Extract Nested Data in Python
Load JSON files with Path and recursively extract values by key from nested Python structures using modern typing and standard library.
import json
from pathlib import Path
from typing import Any, Dict, List, Union
def load_data(filepath: Union[str, Path]) -> Union[Dict[str, Any], List[Any]]:
"""Load JSON data from a file with modern Path handling."""
path = Path(filepath)
if not path.exists():
raise FileNotFoundError(f"File not f…How to Read the Python Path from VS Code settings.json in Python
This code loads VS Code's settings.json file and extracts the python.defaultInterpreterPath value, with a mock demonstration for testing.
import json
from pathlib import Path
from unittest.mock import patch
def read_vscode_python_path(settings_path: Path) -> str:
"""Extract python.defaultInterpreterPath from VS Code settings.json."""
with open(settings_path, "r") as f:
settings = json.load(f)
return settings.get("python", {}).get("d…How to Memoize Pure Functions with functools.lru_cache in Python
Use functools.lru_cache to memoize a pure Fibonacci function and avoid recomputing repeated values.
from functools import lru_cache
@lru_cache(maxsize=128)
def fibonacci(n: int) -> int:
"""Return the nth Fibonacci number (0-indexed) using memoization."""
if n < 2:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
if __name__ == "__main__":
for i in range(10):
print(f"fibonacci({…How to Vectorize a Function with a Pure Python Fallback
Create a decorator that calls a scalar function directly for a single value and routes list inputs to a pure-Python fallback for vectorized processing without NumPy.
import math
def fallback_vectorize(func, fallback=None):
"""Vectorize a scalar function with a pure-Python fallback for lists."""
if fallback is None:
fallback = lambda x: [func(i) for i in x]
def wrapped(*args):
if len(args) == 1 and isinstance(args[0], (list, tuple)):
retur…Using a Python Generator Instead of a List to Save Memory
Compare a list approach with a generator to stream values lazily, avoiding memory-heavy storage of large sequences.
def fibonacci_generator(limit):
a, b = 0, 1
count = 0
while count < limit:
yield a
a, b = b, a + b
count += 1
def sum_first_n(generator, n):
total = 0
for i, value in enumerate(generator):
if i >= n:
break
total += value
return total
if __…Dataclass with Type Hints Fields in Python
Create a data class with typed fields and default values, then instantiate and inspect it.
from dataclasses import dataclass
@dataclass
class Person:
name: str
age: int
email: str = "unknown@example.com"
is_active: bool = True
if __name__ == "__main__":
person = Person(name="Alice", age=30)
print(person)
print(f"Name: {person.name}, Age: {person.age}, Email: {person.email}, A…Design Data Helpers with Python TypedDict and Literal
Use TypedDict, Literal, and Union to define typed data shapes and parse values in Python.
from typing import TypedDict, Literal, Optional, Union, List
class User(TypedDict):
name: str
age: int
role: Literal["admin", "user", "guest"]
def describeUser(data: User) -> str:
return f"{data['name']} ({data['age']}) — {data['role']}"
def parse_value(item: Union[int, str, None]) -> str:
if it…Fix and Test a Regression Bug in Python with Unit Tests
This code implements a circle area function that raises ValueError for negative radii, then runs basic tests and a regression check for that edge case.
import math
def calculate_area(radius):
"""Calculate the area of a circle given its radius."""
if radius < 0:
raise ValueError("Radius cannot be negative")
return math.pi * radius ** 2
def main():
test_cases = [0, 1, 2.5, 5, 10]
print("Circle Area Calculator")
print("-" * 30)
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