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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)Generator Function to Yield an Infinite Counter in Python
This code demonstrates a generator function that yields an infinite sequence of integers starting from a given value, allowing lazy, memory-efficient iteration.
def infinite_counter(start=0):
count = start
while True:
yield count
count += 1
if __name__ == "__main__":
counter = infinite_counter(5)
for _ in range(5):
print(next(counter))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 Repeat a Generator Cycle Single Value in Python
Build a generator that repeats a single value across multiple cycles, each cycle adding an extra repetition to mark its completion.
def repeat_with_cycle(value, cycle_limit, repetitions):
"""
Repeats a single value until reaching a cycle limit,
then yields the value one more time to demonstrate a full cycle.
Args:
value: The single value to repeat.
cycle_limit: Number of repetitions per cycle.
repetitio…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 {
…How to Use List Comprehensions and Generators in Python
Analyze a list of numbers using a list comprehension to square evens, a generator for sum, and a generator expression for the maximum squared value.
def analyze_numbers(numbers):
squared = [n ** 2 for n in numbers if n % 2 == 0]
total = sum(n for n in numbers)
max_squared = max((n ** 2 for n in numbers), default=0)
return squared, total, max_squared
if __name__ == "__main__":
data = [1, 2, 3, 4, 5, 6]
evens_squared, total_sum, max_sq = an…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 …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 Find Missing Values in Large Datasets in Python
Analyze missing values across multiple large pandas DataFrames with counts and percentages.
import pandas as pd
import numpy as np
def find_missing_values_summary(datasets):
"""Analyze missing values across multiple datasets (dict of name: DataFrame)."""
summary = {}
for name, df in datasets.items():
missing_count = df.isnull().sum()
total_rows = len(df)
missing_pct = (mi…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 []
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