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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
How to Check if a String is Numeric in Python
This code provides a function to determine if a string represents a valid numeric value using Python's built-in float() conversion.
def is_numeric(s):
"""Check if a string represents a valid numeric value."""
try:
float(s)
return True
except (ValueError, TypeError):
return False
if __name__ == "__main__":
test_cases = ["123", "-45.67", "3.14e10", "0x1A", "abc", "12.5.6", " 42 ", ""]
for case in test_c…How to Dump a Debugging Repr for Unknown Types in Python
Build a fallback repr that shows dataclass fields or object attributes for any value, handy when debugging unknown types.
import dataclasses
from typing import Any
@dataclasses.dataclass
class Sample:
name: str
values: list[int]
def dump_repr(obj: Any) -> str:
"""Return a concise but complete repr for debugging unknown types."""
if dataclasses.is_dataclass(obj):
fields = ", ".join(
f"{field.name}={…How to Define a Simple Class with __init__ and __repr__ in Python
Defines a Person class with __init__ to store name and age, and __repr__ to give a readable string representation.
class Person:
def __init__(self, name, age):
self.name = name
self.age = age
def __repr__(self):
return f"Person(name='{self.name}', age={self.age})"
if __name__ == "__main__":
p1 = Person("Alice", 30)
p2 = Person("Bob", 25)
print(p1)
print(p2)How to Define a Simple Python Class with __init__ and __repr__
Define a basic Python class with an __init__ method to set instance attributes and a __repr__ method for a readable representation of objects.
class Person:
def __init__(self, name, age):
self.name = name
self.age = age
def __repr__(self):
return f"Person(name={self.name!r}, age={self.age!r})"
if __name__ == "__main__":
person = Person("Alice", 30)
print(person)How to Implement a Stack Class in Python
A complete Stack class implemented with a Python list, featuring push, pop, peek, is_empty, size, and a readable string representation.
class Stack:
def __init__(self):
self._items = []
def push(self, item):
"""Add an item to the top of the stack."""
self._items.append(item)
def pop(self):
"""Remove and return the top item. Raises IndexError if empty."""
if self.is_empty():
raise IndexE…How to Reset Python's Random Seed for Deterministic Output
This code shows how to seed Python's random module to generate identical random sequences across runs, ensuring reproducibility.
import random
def seeded_random_sequence(seed, count=5, low=1, high=100):
random.seed(seed)
return [random.randint(low, high) for _ in range(count)]
if __name__ == "__main__":
seed_value = 42
first_run = seeded_random_sequence(seed_value)
print("First run:", first_run)
# Reset seed and gener…How to Chunk a Long Document for RAG Retrieval in Python
Split text into overlapping chunks at sentence boundaries using a custom Python function suitable for RAG retrieval pipelines.
import re
from pathlib import Path
def chunk_document(text, chunk_size=500, overlap=100):
"""Split text into overlapping chunks suitable for RAG retrieval."""
# Normalize whitespace
text = re.sub(r'\s+', ' ', text).strip()
chunks = []
start = 0
while start < len(text):
end = min(s…How to Generate a Mock devcontainer.json Config in Python
Build a reproducible devcontainer.json file with Python, composing name, image, extensions, forwarded ports, and a post-create command as a dict.
import json
from pathlib import Path
def create_devcontainer_config(
image: str = "mcr.microsoft.com/devcontainers/python:3.11",
name: str = "python-dev-container",
ports: list[int] | None = None,
post_create: str | None = None,
) -> dict:
config = {
"name": name,
"image": image,
…How to Test Hypotheses with Property-Based Check in Python
A Python search that checks an integer property (palindrome divisible by digit sum) and returns the first counterexample within a range, with exactly reproduced output from the code.
def is_property_satisfied(n):
"""
Demonstrates a mathematically inspired property:
checks whether n is both a palindrome and divisible by its digit sum.
"""
s = str(n)
if s != s[::-1]:
return False
digit_sum = sum(int(d) for d in s)
return digit_sum != 0 and n % digit_sum == 0
…How to Build an Adapter to Translate External API Responses in Python
Build an adapter class that translates a mock external API's response shape into your internal representation, keeping callers decoupled from the external contract.
import json
from typing import Dict, Any
class ExternalAPI:
"""Mock external service returning a different data shape."""
def get_user(self, user_id: int) -> Dict[str, Any]:
return {
"id": user_id,
"full_name": "Jane Doe",
"email_address": "jane@example.com",
…Return Proper HTTP Status Codes Table in Python
Mock HTTP status code table with proper numeric and textual representations, including formatted status lines and a filtered table view.
# Mock HTTP status code table with proper numeric and textual representations
codes = {
200: "OK",
201: "Created",
204: "No Content",
301: "Moved Permanently",
302: "Found",
304: "Not Modified",
400: "Bad Request",
401: "Unauthorized",
403: "Forbidden",
404: "Not Found",
50…How to Simulate Trace Sampling Head in Python
Simulate head-based probabilistic trace sampling on mock trace data with a configurable sample rate and optional seed for reproducibility.
import random
def trace_sampling_head(mock_traces, sample_rate=0.5, seed=None):
"""Simulate probabilistic trace sampling (head-based) on mock data.
Args:
mock_traces: list of trace dictionaries with a unique 'trace_id'
sample_rate: float 0.0-1.0, probability of keeping a trace
see…How to Shuffle Items by Group in Python
Randomly shuffle items within each group while keeping groups contiguous, using a seed for reproducible results.
import random
def shuffle_sort_groups(items, group_key, seed=None):
"""Randomize order within groups, keeping groups contiguous."""
rng = random.Random(seed)
groups = {}
for item in items:
key = group_key(item)
groups.setdefault(key, []).append(item)
result = []
for k…How to Build a Mock ML Pipeline with Prefect in Python
Create a lightweight Prefect flow with mock preprocessing, training, and evaluation tasks to prototype an ML pipeline end-to-end.
from prefect import task, flow
from datetime import datetime
@task
def preprocess_data(raw_value: float) -> float:
"""Mock preprocessing: normalize the input value."""
return raw_value / 100.0
@task
def train_model(features: float) -> dict:
"""Mock training: return a fake model artifact."""
return …How to Build an sklearn Pipeline with ColumnTransformer in Python
A mock example showing how to chain preprocessing and a regression model into a single sklearn Pipeline, scaling numeric features and one-hot encoding categorical features with ColumnTransformer.
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression
# Mock dataset
X = np.array([[1, 'red'], [2, 'blue'], [3, 'red'], [4, 'green'], [5, 'blue']], dtype=o…How to Do Random Search for Hyperparameter Tuning in Python
A mock random search that samples hyperparameter combinations from a grid and ranks them by a dummy score, with a reproducible seed.
import random
# Mock random search over a small hyperparameter grid
param_grid = {
"learning_rate": [0.001, 0.01, 0.1],
"batch_size": [16, 32, 64],
"num_layers": [1, 2, 3]
}
def random_search(grid, n_iter=5, seed=42):
"""Perform random search over a hyperparameter grid."""
random.seed(seed)
k…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 do feature selection with VarianceThreshold in Python
This code demonstrates how to use scikit-learn's VarianceThreshold to remove low-variance features from a NumPy array, keeping only those that vary enough to be useful for modeling.
import numpy as np
from sklearn.feature_selection import VarianceThreshold
def main():
# Mock dataset: 4 samples, 5 features
X = np.array([
[0.1, 0.2, 1.0, 1.0, 0.5],
[0.2, 0.2, 0.0, 1.0, 0.4],
[0.1, 0.2, 1.0, 1.0, 0.6],
[0.3, 0.2, 1.0, 0.0, 0.5]
])
# Select features w…One Hot Encode Categories in Python
Convert a list of categorical strings into one-hot encoded numeric vectors using pure Python and NumPy.
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…StandardScaler mock in Python
A pure-Python StandarScaler class that standardizes features to zero mean and unit variance without sklearn.
import math
class StandardScaler:
def __init__(self):
self.mean_ = None
self.std_ = None
def fit(self, X):
n = len(X)
self.mean_ = [sum(col) / n for col in zip(*X)]
self.std_ = []
for col in zip(*X):
variance = sum((x - self.mean_[i]) ** 2 for i, x …How to Mock Stratified Assignment by Segment in Python
Simulate stratified assignment for A/B experiments by sampling a fixed proportion of units from each segment, with deterministic seeds for reproducibility.
import random
def stratified_assignment(segments, seed=None):
"""
Mock stratified assignment: given a dict of segment -> population size,
return a dict of segment -> sampled unit ids (deterministic with seed).
"""
if seed is not None:
random.seed(seed)
rng = random.Random(seed)
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Guide: free Python code samples library
Copy-ready Python snippets for learners and developers
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- Open a sample, read How it works, and copy the code block
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