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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
How to check for None and raise helpful errors in Python
A defensive function that explicitly validates data, keys, and values — raising descriptive ValueError and KeyError exceptions before returning a result.
def get_value(data, key):
if data is None:
raise ValueError("data cannot be None")
if key not in data:
raise KeyError(f"key '{key}' not found in data")
result = data[key]
if result is None:
raise ValueError(f"value for key '{key}' is None")
return result
if __name__ == "__…How to define an exception hierarchy for domain errors in Python
Create a custom exception hierarchy with a base DomainError class and specific subclasses to handle validation, not-found, permission, and concurrency errors cleanly in Python apps.
class DomainError(Exception):
"""Base class for all domain errors."""
pass
class ValidationError(DomainError):
"""Raised when input data fails validation rules."""
pass
class NotFoundError(DomainError):
"""Raised when a requested entity does not exist."""
pass
class PermissionDeniedError(Dom…Automatically Highlight Data Validation Errors Inside Excel Files in Python
Load an Excel file with openpyxl, iterate over cells, and highlight invalid data (empty, negative) with a red fill and error message.
import openpyxl
from openpyxl.styles import PatternFill
from pathlib import Path
def highlight_validation_errors(filepath: str, output_path: str = None):
wb = openpyxl.load_workbook(filepath)
red_fill = PatternFill(start_color="FF0000", end_color="FF0000", fill_type="solid")
for sheet in wb.worksheet…How to Validate JSON Schema Shape in Python
Validate JSON data against a schema using manual checks for required fields, types, and constraints.
import json
from typing import Any, Dict
def validate_person_schema(data: Dict[str, Any]) -> bool:
"""Validate a person object against expected schema shape."""
if not isinstance(data, dict):
return False
# Required fields check
required_fields = {"name", "age", "email"}
if not requir…How to Validate a JSON File in Python
A beginner-friendly Python helper that reads a JSON file, catches common errors, and returns a status dictionary.
import json
from pathlib import Path
def get_valid_json_data(file_path: str) -> dict:
file = Path(file_path)
if not file.exists():
return {"status": "error", "message": f"File not found: {file_path}"}
try:
data = json.loads(file.read_text())
except json.JSONDecodeError as e:
…How to Check if a Set is a Subset in Python
Check whether one set contains all elements of another set using the issubset method.
def is_subset(allowed_set, check_set):
"""
Check if check_set is a subset of allowed_set.
Returns True if all elements of check_set are in allowed_set, otherwise False.
"""
return check_set.issubset(allowed_set)
if __name__ == "__main__":
# Example usage
allowed = {1, 2, 3, 4, 5}
valid…How to Validate JSON Types per Key in Python
Load a JSON object and validate the type of each key against an expected schema, reporting missing or mismatched fields.
import json
from typing import Any, Dict, Type
def validate_json_types(data: Dict[str, Any], schema: Dict[str, Type]) -> Dict[str, str]:
"""Validate that each key in data matches the expected type in schema."""
errors = {}
for key, expected_type in schema.items():
if key not in data:
e…How to Validate Required Dict Keys in Python
Check whether a dictionary contains all required keys and return the list of missing ones using a simple list comprehension.
def find_missing_keys(data: dict, required_keys: list) -> list:
"""Return a list of required keys that are missing from the dictionary."""
return [key for key in required_keys if key not in data]
if __name__ == "__main__":
user_data = {
"name": "Alice",
"email": "alice@example.com",
…How to Validate Text and Count Words in Python
Count word frequencies, find unique and repeated words in a text using Python dictionaries and sets for beginner text validation.
def validate_text(text):
words = text.lower().split()
word_counts = {}
for word in words:
cleaned = word.strip('.,!?;:"\'')
if cleaned:
word_counts[cleaned] = word_counts.get(cleaned, 0) + 1
unique_words = set(word_counts.keys())
repeated_words = {word for word…Validate dictionary data with sets in Python
Validate a dictionary against required keys and allowed value sets, returning a list of validation errors.
def validate_data(data, required_keys, allowed_values=None):
"""
Validate a dictionary against required keys and optional allowed value sets.
Returns a list of validation errors (empty list if valid).
"""
errors = []
# Check for missing required keys
missing = set(required_keys) - set(…Add property getter setter validation in Python
Shows how to use @property with a setter to validate values before assigning them in a Python class.
class Temperature:
def __init__(self, celsius=0):
self._celsius = celsius # Use underscore to avoid recursion
@property
def celsius(self):
"""Getter returns the stored value."""
return self._celsius
@celsius.setter
def celsius(self, value):
"""Setter valid…How to Create an Immutable Money Class in Python with dataclasses
Define a frozen dataclass Money that holds an amount and currency, enforces non-negative amounts, and supports safe addition across matching currencies.
from dataclasses import dataclass
@dataclass(frozen=True)
class Money:
amount: float
currency: str = "USD"
def __post_init__(self) -> None:
if self.amount < 0:
raise ValueError("amount must be non-negative")
def add(self, other: "Money") -> "Money":
if self.currency != o…How to Validate Data Types in Python with a Class
A beginner-friendly Python class that checks if a value is a string, integer, float, list, or empty, using simple methods and isinstance checks.
class DataValidator:
"""A simple data validation helper for beginners."""
def __init__(self, data):
self.data = data
def is_string(self):
return isinstance(self.data, str)
def is_integer(self):
return isinstance(self.data, int) and not isinstance(self.data, bool)
…How to Validate User Input with a Dataclass in Python
A dataclass stores name, age, and email, and a validator class checks each field, returning a dictionary of boolean results.
from dataclasses import dataclass
@dataclass
class UserInput:
name: str
age: int
email: str
def is_valid_name(self) -> bool:
return bool(self.name.strip()) and len(self.name.strip()) >= 2
def is_valid_age(self) -> bool:
return isinstance(self.age, int) and 0 < self.age < 150
…Validate dataclass fields with __post_init__ in Python
Add custom validation to a Python dataclass inside __post_init__, raising ValueError or TypeError for invalid field values.
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class Product:
name: str
price: float
quantity: int = 1
category: Optional[str] = None
def __post_init__(self):
if not self.name or not isinstance(self.name, str):
raise ValueError("name must be a…Validate Sudoku Board Rows Columns and Boxes in Python
Validate a 9x9 Sudoku board by checking that each row, column, and 3x3 box contains the numbers 1 through 9 exactly once.
def validate_sudoku(board):
def is_valid_group(group):
return sorted(group) == list(range(1, 10))
def get_columns():
return [[board[r][c] for r in range(9)] for c in range(9)]
def get_boxes():
boxes = []
for box_row in range(0, 9, 3):
for box_col in range(0, 9,…How to Validate Data with Python Comprehensions and Generators
Use list, generator, and dictionary comprehensions to filter and transform data for quick validation in Python.
def validate_integer(data):
return [item for item in data if isinstance(item, int)]
def validate_positive(numbers):
return (num for num in numbers if num > 0)
def validate_string_lengths(data, min_length=3):
return {item: len(item) for item in data if isinstance(item, str) and len(item) >= min_length}
i…How to Validate JSON Output Against a Dict Schema in Python
Validate JSON-like data against a simple dict schema with type checking and descriptive error messages using only the Python standard library.
from typing import Dict, Any, List, Union
def validate_json(data: Any, schema: Dict[str, str]) -> List[str]:
"""
Validate JSON-like data against a simple dict schema.
Schema format: {field_name: expected_type} where type is one of:
'str', 'int', 'float', 'bool', 'list', 'dict', 'any'
Returns list …How to Validate LLM Output in Python
A beginner-friendly DataValidator class that checks required fields and type constraints on LLM-generated or user JSON data.
import json
from typing import Any, Dict, List, Optional
class DataValidator:
"""Simple helper for validating LLM-generated or user data."""
def __init__(self, required_fields: List[str], schema: Optional[Dict[str, str]] = None):
self.required_fields = required_fields
self.schema = schema or…How to Validate SSL Certificates for Multiple Domains in Python
A Python utility that checks SSL certificate expiry dates for a list of domains using the standard library ssl and socket modules.
import ssl
import socket
from datetime import datetime
def check_ssl_certificate(hostname: str, port: int = 443) -> dict:
"""Validate SSL certificate for a given hostname."""
context = ssl.create_default_context()
with socket.create_connection((hostname, port), timeout=5) as sock:
with context.wra…How to validate argparse CLI commands in Python
Build a beginner-friendly command-line argument parser with argparse, including required and optional arguments, plus simple validation for age.
import argparse
def main():
parser = argparse.ArgumentParser(description="Validate CLI arguments for beginners.")
parser.add_argument("name", type=str, help="Your name.")
parser.add_argument("--age", type=int, default=None, help="Your age (optional).")
parser.add_argument("--verbose", action="store_t…Check Null Rate Threshold in PySpark DataFrame
This PySpark code checks the null rate of specified DataFrame columns against a threshold and returns violations.
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, count
def check_null_rate(df, threshold=0.2, columns=None):
"""
Check null rate for specified columns (or all) against a threshold.
Returns columns that exceed the threshold.
"""
cols = columns or df.columns
total…How to Validate Data in a Python Pipeline
A helper module to validate common record types — email, positive integer, and non-empty string list — before processing data in a pipeline.
from typing import Any, Iterable
def is_valid_email(email: str) -> bool:
"""Basic email check: one '@', no spaces, dot after '@'."""
if "@" not in email or " " in email:
return False
local, _, domain = email.partition("@")
return bool(local) and "." in domain
def is_positive_int(value: Any)…How to Validate Fact Table Grain Row Counts in Python
Validate fact table grain by checking dimension key references, unique grain combinations, duplicate rows, and dimension cardinality from a CSV file.
import csv
import hashlib
from pathlib import Path
def validate_fact_grain(fact_file: Path, expected_dim_keys: dict[str, set[str]]) -> dict:
"""
Validate fact table grain by checking each row's dimension keys exist
in expected dimension tables and row count consistency.
"""
dim_references = {}
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