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How to Compare Strings with casefold in Python
Compares two strings ignoring case differences using the casefold() method for proper Unicode normalization.
def compare_strings(str1: str, str2: str) -> bool:
return str1.casefold() == str2.casefold()
if __name__ == "__main__":
tests = [
("HELLO", "hello"),
("Straße", "STRASSE"),
("Python", "Python"),
("Mixed Case", "mixed case"),
]
for s1, s2 in tests:
print(f"{s1!r}…How to Normalize Text in Python
This code defines a function that trims, lowercases, and collapses extra whitespace in a string, returning normalized text.
def normalize_text(text: str) -> str:
normalized = " ".join(text.lower().strip().split())
return normalized
if __name__ == "__main__":
raw = " Hello, WORLD! This is a test. "
print(normalize_text(raw))How to Replace Multiple Spaces with a Single Space in Python
This snippet uses the `re` module to collapse runs of consecutive spaces in a string into a single space, cleaning up whitespace.
import re
def collapse_spaces(text):
"""Replace multiple consecutive spaces with a single space."""
return re.sub(r' +', ' ', text)
if __name__ == "__main__":
sample = "This has multiple spaces between words."
result = collapse_spaces(sample)
print(f"Original: '{sample}'")
print(f"Co…Normalize unicode accents to ASCII in Python
This code converts accented Unicode characters to ASCII equivalents using the standard library's unicodedata module.
import unicodedata
def normalize_accents(text: str) -> str:
"""Convert accented unicode characters to ASCII equivalents."""
decomposed = unicodedata.normalize('NFD', text)
ascii_text = ''.join(
char for char in decomposed
if unicodedata.category(char) != 'Mn'
)
return unicodedata.n…Text Processor Functions for Beginners in Python
Demonstrates simple text-processing utilities: word counting, word reversal, whitespace normalization, and lowercase conversion using basic string methods.
def count_words(text):
"""Return the number of words in a string."""
return len(text.split())
def reverse_words(text):
"""Return the text with words in reverse order."""
return ' '.join(text.split()[::-1])
def remove_extra_spaces(text):
"""Return text with extra whitespace collapsed to a single s…How to Normalize a List of Numbers in Python
This Python function normalizes a list of numeric values to the range [0, 1] using min-max scaling, returning a new list and leaving the original unchanged.
def normalize(data):
"""
Normalize a list of numeric values to the range [0, 1].
Returns a new list, leaving the original unchanged.
"""
if not data:
return []
min_val = min(data)
max_val = max(data)
# Handle the edge case where all values are identical
if min_val …How to Normalize a List of Numbers to the 0-1 Range in Python
Scale a list of numbers so the minimum becomes 0 and the maximum becomes 1 using min-max normalization.
def min_max_normalize(values):
"""Normalize a list of numbers to the [0, 1] range."""
if not values:
return []
min_val = min(values)
max_val = max(values)
if min_val == max_val:
return [0.0] * len(values)
return [(x - min_val) / (max_val - min_val) for x in values]
if __name__…How to Standardize a List with Z-Score Normalization in Python
This code computes the z-score for each number in a list, standardizing the data to have zero mean and unit variance using the statistics module.
import statistics
def z_score_normalize(values):
"""Standardize a list of numbers using z-score normalization."""
if not values or len(values) < 2:
raise ValueError("Need at least 2 values for meaningful z-score normalization")
mean = statistics.mean(values)
std_dev = statistics.stdev(val…How to Write a Normalize Function with Default Parameters in Python
Define a reusable normalize function with configurable default parameters for lowercase conversion, whitespace stripping, and punctuation removal.
def normalize(text, lowercase=True, strip_whitespace=True, remove_punctuation=False):
"""Normalize a string based on configurable options."""
if lowercase:
text = text.lower()
if strip_whitespace:
text = text.strip()
if remove_punctuation:
text = ''.join(char for char in text if…Build a Simple ETL Pipeline in Python
A simple ETL pipeline that reads JSON Lines, transforms records with filtering and normalization, and writes the result to JSON.
import json
from pathlib import Path
def read_input(file_path: Path) -> list[dict]:
"""Read JSON lines file into list of dicts."""
with file_path.open("r", encoding="utf-8") as f:
return [json.loads(line) for line in f if line.strip()]
def transform(records: list[dict]) -> list[dict]:
"""Transf…How to Normalize Data in Python with Dictionaries and Sets
Normalize a list of dicts by keeping selected keys, stripping/lowercasing strings, and extracting unique sorted values using set comprehension.
def normalize_data(data, keys):
"""
Normalize a list of dictionaries by keeping only specified keys
and converting values to proper types.
"""
normalized = []
for item in data:
clean_item = {}
for key in keys:
value = item.get(key)
if isinstance(value, st…How to Normalize Data with Dictionaries and Sets in Python
Normalize dictionary entries to a fixed set of keys and extract unique values using sets in Python.
def normalize_entry(entry: dict, valid_keys: set) -> dict:
result = {}
for key in valid_keys:
result[key] = entry.get(key, "")
return result
def unique_values(entries: list[dict], key: str) -> set:
return {entry.get(key) for entry in entries if entry.get(key) is not None}
if __name__ == "__…How to Transform a List of Dictionaries with Sets in Python
Normalize a list of dict records — cleaning names, extracting unique tags with sets, and building a standardized result.
def transform_data(raw_records):
"""Transform a list of dict records into normalized data with sets for unique values."""
normalized = []
unique_names = set()
all_tags = set()
for record in raw_records:
# Normalize name to lowercase and strip whitespace
name = record.get("name"…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…How to Build a Docker Image Tag Script in Python
Generate consistent Docker image tags from service names and versions with automatic normalization.
#!/usr/bin/env python3
"""Mock script for building docker image tags."""
def build_tag(service_name: str, version: str, registry: str = "docker.io") -> str:
"""Construct a docker image tag."""
safe_name = service_name.lower().replace("_", "-")
return f"{registry}/{safe_name}:{version}"
if __name__ == "…How to Clean and Format Data in Python
This code loads JSON data, cleans records by removing empty fields and normalizing text, then summarizes the results with counts and unique keys.
import json
from pathlib import Path
def load_data(filepath: str) -> dict:
"""Load JSON data from a file."""
with Path(filepath).open("r", encoding="utf-8") as f:
return json.load(f)
def clean_records(records: list[dict]) -> list[dict]:
"""Remove empty fields and normalize text to lowercase."""…Normalize Timestamps to UTC DateTime in Python
Convert timestamps in multiple formats to UTC-aware datetime objects using datetime.strptime and astimezone.
from datetime import datetime, timezone
raw_timestamps = [
"2024-01-15 14:30:00+02:00",
"17/05/2024 09:15:00 -0500",
"2024-03-01T22:45:00Z",
"2024-06-20 08:00:00+09:30"
]
def parse_and_convert(ts: str) -> datetime:
normalized_ts = ts.strip().replace("Z", "+00:00")
formats = [
"%Y-%m-%…How to Build a Pipe and Filter Text Processing Chain in Python
A functional pipe-and-filter chain that transforms text through uppercase, whitespace normalization, number removal, stopword filtering, and file export.
import re
import sys
def pipe_filter_chain(stream):
def uppercase(text):
return text.upper()
def strip_whitespace(text):
return " ".join(text.split())
def remove_numbers(text):
return re.sub(r"\d+", "", text)
def remove_stopwords(text, stopwords={"the", "and", "of", "in"}):…How to create a stable cache key from function arguments in Python
Generate a stable SHA-256 cache key from normalized function arguments, with keyword order normalized and tests using mocks.
import hashlib
import json
from unittest.mock import Mock
def make_cache_key(*args, **kwargs):
"""Normalize args/kwargs into a stable hash key for caching."""
normalized = {
"args": [repr(arg) for arg in args],
"kwargs": {key: repr(value) for key, value in sorted(kwargs.items())}
}
pa…How to Convert Data with Scaling for Database Optimization in Python
A beginner-friendly helper that normalizes and scales numeric fields in a list of dicts, reducing storage footprint for database efficiency.
import json
from datetime import datetime
def convert_data(data: list[dict], scale_factor: int = 1) -> list[dict]:
"""Convert a list of dicts to a scaled, normalized format for database efficiency."""
converted = []
for row in data:
normalized = {}
for key, value in row.items():
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