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How to Check and Manipulate Strings in Python
Demonstrates core string inspection and transformation methods like case conversion, trimming, splitting, and membership checks on a sample string.
text = " Hello, Python Learners! "
print(f"Original: '{text}'")
print(f"Lowercase: '{text.lower()}'")
print(f"Uppercase: '{text.upper()}'")
print(f"Title case: '{text.title()}'")
print(f"Stripped: '{text.strip()}'")
print(f"Length: {len(text)}")
print(f"Replace: '{text.replace('Python', 'Programming')}'")
print(f"S…How to Convert camelCase to snake_case in Python
Convert camelCase strings to snake_case using a simple Python function that inserts underscores before uppercase letters and lowercases everything.
def camel_to_snake(s):
result = ""
for i, char in enumerate(s):
if char.isupper() and i > 0:
result += "_"
result += char.lower()
return result
if __name__ == "__main__":
test_cases = ["camelCase", "helloWorld", "thisIsACoolExample", "already_snake", "UPPER"]
for case i…How to Inspect String Statistics in Python
A beginner-friendly function that returns detailed statistics about a string, including length, word count, character types, and easy text transformations.
def inspect_text(text: str) -> dict:
"""Return useful stats about a string for beginners."""
words = text.split()
return {
"length": len(text),
"word_count": len(words),
"uppercase": sum(1 for ch in text if ch.isupper()),
"lowercase": sum(1 for ch in text if ch.islower()),
…How to Transform Text in Python with a Helper Function
Build a simple Python helper to strip extra whitespace and convert text to upper, lower, or title case.
def transform_text(text, upper=False, lower=False, strip_whitespace=False, title_case=False):
"""Apply common string transformations for beginners."""
result = text
if strip_whitespace:
result = " ".join(result.split())
if upper and lower:
raise ValueError("Cannot apply both upper and…Replace Negative Values in a List with Python
This code defines a function that replaces every negative number in a list with a replacement value, defaulting to zero, using a list comprehension.
def replace_if_negative(values, replacement=0):
return [replacement if value < 0 else value for value in values]
if __name__ == "__main__":
numbers = [5, -3, 8, -1, 0, -7, 2]
result = replace_if_negative(numbers)
print(f"Original: {numbers}")
print(f"Replaced: {result}")How to Assert an Invariant After a Complex Transformation in Python
Use assert to verify that a multi-step transformation preserves a mathematical invariant, catching regressions early.
def transform_value(value):
"""Apply several transformations to a value."""
doubled = value * 2
shifted = doubled + 10
normalized = shifted / 2
return int(normalized)
def assert_invariant(value):
"""Assert that the transformation preserves a key invariant."""
original = value
transform…How to Map Dictionary Values with a Transformation Function in Python
Create a reusable function that applies a transformation to every value in a dictionary and returns a new dict.
def transform_dict_values(d, func):
"""Apply a transformation function to every value in a dictionary."""
return {key: func(value) for key, value in d.items()}
if __name__ == "__main__":
original = {"a": 1, "b": 2, "c": 3}
doubled = transform_dict_values(original, lambda x: x * 2)
print(doubled)
…How to Use List Comprehensions and Generators to Transform Data in Python
Transform a list of integers by squaring even numbers with a list comprehension and cubing odd numbers with a generator.
def transform_data(data):
"""
Transform a list of integers:
- squares of even numbers using a list comprehension
- cubes of odd numbers using a generator
"""
squares = [num ** 2 for num in data if num % 2 == 0]
cubes = (num ** 3 for num in data if num % 2 != 0)
return squares, cubes
i…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…Pivot long to wide transformation dict
Transform a list of dictionaries from long format to wide format by pivoting on a key column and aggregating values, using pure Python.
def pivot_long_to_wide(rows, key_col, value_col, id_cols=None):
"""
Convert long-format data (list of dicts) to wide format.
Args:
rows: List of dicts in long format
key_col: Column name to pivot on (becomes new column headers)
value_col: Column name whose values become the cel…How to Build an Anti-Corruption Layer in Python
Translate messy legacy system data into a clean domain model using an anti-corruption layer in Python.
class MockLegacySystem:
"""Simulates a legacy system with messy data formats."""
def get_user_data(self):
# Legacy format: fields are abbreviated and types are inconsistent
return {
"usr_id": "USR-123",
"usr_nm": "john_doe",
"email_addrs": "John.Doe@example.c…How to Explode an Array Column in Python
This code demonstrates a mock explode operation that converts an array column into multiple rows, similar to Spark's explode function.
import json
def explode_array_column(data, column):
"""Mock explode: split array column into multiple rows."""
exploded = []
for row in data:
values = row.get(column, [])
for value in values:
new_row = dict(row)
new_row[column] = value
exploded.append(n…Mock RDD in Python: Simulate Spark RDD Lazy Transformations
Simulate Apache Spark RDD behavior in Python with lazy maps, filters, partitions, and a collect action.
import random
def mock_rdd(data, num_slices=2):
"""
A simple simulation of Spark RDD behavior with lazy evaluation,
transformations, and an action.
"""
class SimpleRDD:
def __init__(self, data, num_slices=2):
self.data = data
self.num_slices = num_slices
…How to Expand a Contract and Migrate Data in Python
Expand an old data contract by renaming fields and adding defaults, then migrate to a final version with deepcopy isolation.
import json
from copy import deepcopy
# Mock data representing a user record (old contract)
old_contract = {
"id": 1,
"name": "Alice",
"email": "alice@example.com",
"age": 30,
"status": "active"
}
# Expanded contract: adds fields with defaults and renames some fields
expand_rules = {
"id": "u…Browse by section
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