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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…Filter List to Keep Only Whitelist Values in Python
Filter a list of values to keep only those present in a predefined whitelist set using a list comprehension.
def filter_whitelist(values, whitelist):
"""Return only values that are present in the whitelist set."""
return [value for value in values if value in whitelist]
if __name__ == "__main__":
raw_values = ["apple", "banana", "cherry", "date", "apple", "elderberry"]
allowed = {"apple", "banana", "date"}
…Find Missing Numbers, Duplicates, and Ranges in Python
Analyze a list to identify missing numbers, duplicate values, and contiguous ranges using sets and the Counter class.
def find_missing_duplicates_ranges(numbers):
"""Find missing numbers, duplicates, and ranges in a list."""
from collections import Counter
if not numbers:
return {"missing": [], "duplicates": [], "ranges": []}
full_range = set(range(min(numbers), max(numbers) + 1))
present = set(n…Find the Duplicate Number in Python Using Floyd's Cycle Detection
Detects the duplicate integer in an array of n+1 numbers (values 1 to n) in O(n) time and O(1) space using Floyd's cycle detection algorithm applied to a linked-list model.
def find_duplicate(nums):
slow = nums[0]
fast = nums[0]
# Phase 1: Find intersection point of the cycle
while True:
slow = nums[slow]
fast = nums[nums[fast]]
if slow == fast:
break
# Phase 2: Find the start of the cycle (the duplicate)
slow = nums[0…Generate Pascal's Triangle Rows in Python
Builds Pascal's triangle as a list of rows, where each inner value is the sum of the two values above it.
def generate_pascals_triangle(rows):
triangle = []
for row_num in range(rows):
row = [1] * (row_num + 1)
for col in range(1, row_num):
row[col] = triangle[row_num - 1][col - 1] + triangle[row_num - 1][col]
triangle.append(row)
return triangle
if __name__ == "__main__":
…How to Add Two Lists Elementwise in Python
Add two equal-length lists element by element using a list comprehension with zip, returning a new list of summed values.
def elementwise_add(list1, list2):
return [a + b for a, b in zip(list1, list2)]
if __name__ == "__main__":
list_a = [1, 2, 3, 4]
list_b = [10, 20, 30, 40]
result = elementwise_add(list_a, list_b)
print(result)How to Compare Two Lists Elementwise for Greater Flags in Python
Compare two equal-length lists element by element and return a list of booleans marking where list_a values are greater than list_b values.
def compare_lists_greater(list_a, list_b):
"""
Compare two lists elementwise and return a list of booleans
indicating whether each element in list_a is greater than the
corresponding element in list_b.
"""
if len(list_a) != len(list_b):
raise ValueError("Lists must have the same length"…How to Flatten List of Dict Values in Python
This code flattens the values of a list of dictionaries into a single list, handling both list values and scalar values.
def flatten_dict_values(dicts):
flattened = []
for d in dicts:
for value in d.values():
if isinstance(value, list):
flattened.extend(value)
else:
flattened.append(value)
return flattened
if __name__ == "__main__":
data = [
{"a": …How to Implement a Moving Average from a Data Stream in Python
Implement a MovingAverage class using a deque and running sum to compute the average of the last k values from a continuous data stream.
from collections import deque
class MovingAverage:
def __init__(self, size):
self.size = size
self.queue = deque()
self.window_sum = 0
def next(self, val):
self.queue.append(val)
self.window_sum += val
if len(self.queue) > self.size:
self.window_su…How to Remove Banned Values from a List in Python
Filters a list by removing elements present in a banned set, preserving the original order.
def remove_banned(values, banned):
banned_set = set(banned)
return [item for item in values if item not in banned_set]
if __name__ == "__main__":
values = [1, 2, 3, 4, 5, 2, 6, 3, 7]
banned = [2, 3]
result = remove_banned(values, banned)
print(result)How to Replace Outliers Beyond Threshold with Cap in Python
Replace values that fall below a lower threshold or above an upper threshold by capping them to the threshold values using a simple Python function.
def replace_outliers_with_cap(data, lower_threshold=None, upper_threshold=None):
"""Replace values beyond given thresholds with the threshold values (capping)."""
if lower_threshold is None and upper_threshold is None:
raise ValueError("At least one threshold must be provided.")
capped_data = …Implement Insert Delete GetRandom O(1) in Python
Build a RandomizedSet class that supports insert, delete, and get_random in average O(1) time using a list and a dictionary mapping values to indices.
import random
class RandomizedSet:
def __init__(self):
self.values = []
self.index_map = {}
def insert(self, val):
if val in self.index_map:
return False
self.index_map[val] = len(self.values)
self.values.append(val)
return True
def delete(self…Insert Multiple Values Into a Sorted List in Python
Insert multiple values into an already-sorted list while keeping it sorted using the bisect.insort function.
import bisect
def insert_sorted(sorted_list, values):
for value in values:
bisect.insort(sorted_list, value)
return sorted_list
if __name__ == "__main__":
original = [1, 3, 5, 7, 9]
new_values = [4, 6, 2, 8, 0]
result = insert_sorted(original, new_values)
print(f"Original: {original}"…Sort Unique Values by Frequency in Python
Count element frequencies with Counter and sort unique values by descending frequency, breaking ties alphabetically.
from collections import Counter
def sort_unique_by_frequency(values):
counts = Counter(values)
return sorted(counts.keys(), key=lambda x: (-counts[x], x))
if __name__ == "__main__":
data = [4, 2, 2, 8, 3, 3, 1, 3, 5, 5, 5, 5, 1]
result = sort_unique_by_frequency(data)
print(f"Sorted unique values…Generate UUID4 Values with a Python Generator
This code defines a generator function that yields mock UUID4 values, allowing you to stream unique identifiers one at a time.
import uuid
def generate_uuids(count=5):
"""Generate a stream of mock UUID4 values."""
for _ in range(count):
yield uuid.uuid4()
if __name__ == "__main__":
# Generate and print 5 UUIDs
for uid in generate_uuids(5):
print(uid)Group Consecutive Keys in Python with itertools.groupby
Group consecutive equal elements in a list using the itertools.groupby generator, printing each key and its values.
from itertools import groupby
data = [1, 1, 2, 2, 3, 1, 1, 4, 4, 4]
for key, group in groupby(data):
group_list = list(group)
print(f"Key: {key}, Values: {group_list}")How to Accumulate Values with a Generator in Python
This generator yields the running total of an iterable's elements, producing a cumulative sum with each step.
def accum(iterable):
total = 0
for item in iterable:
total += item
yield total
# Demo
if __name__ == "__main__":
data = [1, 2, 3, 4, 5]
print(list(accum(data))) # [1, 3, 6, 10, 15]
# Also works with any iterable, e.g., range
print(list(accum(range(1, 6)))) # [1, 3, 6, 10, 15]How to Create an Infinite Arithmetic Sequence Generator in Python
Build a memory-efficient generator that yields an infinite arithmetic progression and extract the first N values with list comprehension.
"""Count generator infinite arithmetic progression"""
def arithmetic_counter(start=0, step=1):
"""Generate an infinite arithmetic sequence."""
current = start
while True:
yield current
current += step
if __name__ == "__main__":
counter = arithmetic_counter(1, 3)
result = [next(c…How to Send Values into a Python Generator Coroutine
Use the .send() method to pass values into a running generator coroutine and capture them.
def coroutine():
received = []
while True:
value = yield
received.append(value)
print(f"Coroutine received: {value}")
if value == "stop":
break
return received
if __name__ == "__main__":
gen = coroutine()
next(gen) # Prime the generator
gen.send("he…How to Use Comprehensions and Generators to Check Data in Python
A beginner-friendly helper that filters numeric values, computes squares and cubes with comprehensions and a generator, and returns a summary dictionary.
def check_data(iterable):
"""Return a summary of numeric data using comprehensions and a generator."""
values = [item for item in iterable if isinstance(item, (int, float))]
squares = [x ** 2 for x in values if x > 0]
cubes = (x ** 3 for x in values if x > 0)
cube_list = list(cubes)
return {
…Merge Sorted Iterators with a Heap Generator in Python
Merge multiple sorted iterators into a single sorted stream using a heap and generator, yielding values lazily in order.
import heapq
def merge_sorted_iterators(*iterators):
heap = []
for idx, iterator in enumerate(iterators):
try:
value = next(iterator)
heapq.heappush(heap, (value, idx, iterator))
except StopIteration:
continue
while heap:
value, idx, iterator = …Normalize Data in Python with Comprehensions and Generators
Clean a list by dropping None values with a comprehension, then min-max normalize it using a lazy generator expression — a beginner-friendly data preparation pattern.
import statistics
# Sample raw data including missing and outlier-ish values
raw = [22, 18, None, 25, 30, 19, 22, 17, None, 28, 24]
# Clean the data: drop None values using a list comprehension
clean = [x for x in raw if x is not None]
# Normalize using min-max scaling with a generator expression
min_val = min(clea…Python Generator to Filter Duplicates with a Seen Set
A lazily-evaluated generator function that yields only the first occurrence of each item, using a set to track seen values.
def unique_generator(items):
seen = set()
for item in items:
if item not in seen:
seen.add(item)
yield item
if __name__ == "__main__":
data = [1, 2, 2, 3, 3, 3, 4, 5, 5]
result = list(unique_generator(data))
print(result)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 …Browse by section
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