intermediate⏱55 minutesLección 6 de 7
Declarative Patterns
Explore declarative programming patterns: list comprehensions, generators, expressing intent over mechanics
Declarative Patterns
Declarative programming is about expressing what you want to accomplish rather than how to accomplish it. Python's comprehensions, generators, and built-in functions make declarative style natural and powerful.
Comprehensions: Python's Declarative Powerhouse
Comprehensions are the most Pythonic declarative tool. They transform one iterable into another with clear, intent-revealing syntax.
python
from typing import List, Dict, Set, Any
# List comprehensions
numbers = [1, 2, 3, 4, 5]
# IMPERATIVE
squares_imperative = []
for n in numbers:
squares_imperative.append(n ** 2)
# DECLARATIVE (list comprehension)
squares = [n ** 2 for n in numbers]
print(squares) # [1, 4, 9, 16, 25]
# With conditional
evens = [n for n in numbers if n % 2 == 0]
print(evens) # [2, 4]
# With conditional expression
even_odd = ["even" if n % 2 == 0 else "odd" for n in numbers]
print(even_odd) # ["odd", "even", "odd", "even", "odd"]
# Nested loops
pairs = [(x, y) for x in range(3) for y in range(3)]
print(pairs)
# Flatten a matrix
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
flat = [item for row in matrix for item in row]
print(flat) # [1, 2, 3, 4, 5, 6, 7, 8, 9]
# Dict comprehensions
squares_dict = {n: n ** 2 for n in range(5)}
print(squares_dict) # {0: 0, 1: 1, 2: 4, 3: 9, 4: 25}
# Filter and transform in dict
names = ["Alice", "Bob", "Charlie", "Diana"]
name_lengths = {name: len(name) for name in names if len(name) > 3}
print(name_lengths) # {"Alice": 5, "Charlie": 7, "Diana": 5}
# Set comprehensions
unique_lengths = {len(name) for name in names}
print(unique_lengths) # {3, 5, 7}Generator Expressions
Generators produce values lazily — one at a time — which is crucial for large or infinite sequences.
python
from typing import Generator, List
import sys
# Generator expression (lazy)
numbers = [1, 2, 3, 4, 5]
squares_gen = (n ** 2 for n in numbers)
print(squares_gen) # <generator object at ...>
print(list(squares_gen)) # [1, 4, 9, 16, 25]
# Memory efficiency: compare list vs generator
big_range = range(1000000)
list_squares = [x ** 2 for x in big_range] # ~8MB list
gen_squares = (x ** 2 for x in big_range) # ~56 bytes
print(f"List size: {sys.getsizeof(list_squares):,} bytes")
print(f"Gen size: {sys.getsizeof(gen_squares):,} bytes")
# Generator with yield — more complex logic
def fibonacci(n: int) -> Generator[int, None, None]:
a, b = 0, 1
for _ in range(n):
yield a
a, b = b, a + b
fib = fibonacci(10)
print(list(fib)) # [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
# Infinite generator
def count_from(start: int = 0, step: int = 1) -> Generator[int, None, None]:
current = start
while True:
yield current
current += step
from itertools import islice
first_10 = islice(count_from(0, 3), 10)
print(list(first_10)) # [0, 3, 6, 9, 12, 15, 18, 21, 24, 27]
# Generator pipelines — chain generators
def read_lines() -> Generator[str, None, None]:
yield " Hello, World! "
yield "Python is great"
yield " DECLARATIVE "
def strip_lines(lines: Generator[str, None, None]) -> Generator[str, None, None]:
for line in lines:
yield line.strip()
def lowercase_lines(lines: Generator[str, None, None]) -> Generator[str, None, None]:
for line in lines:
yield line.lower()
pipeline = lowercase_lines(strip_lines(read_lines()))
print(list(pipeline)) # ["hello, world!", "python is great", "declarative"]Note
Generators are single-use. Once exhausted, they yield no more values. If you need to reuse the data, convert to a list or create a new generator.
Generator Pipeline for Data Processing
python
from typing import Generator, Dict, Any
import csv
from io import StringIO
csv_data = """name,age,score,active
Alice,25,85,true
Bob,17,72,true
Charlie,30,91,false
Diana,22,95,true
Eve,28,60,true"""
def parse_csv(data: str) -> Generator[Dict[str, str], None, None]:
reader = csv.DictReader(StringIO(data))
for row in reader:
yield row
def filter_active(rows: Generator) -> Generator:
for row in rows:
if row.get("active") == "true":
yield row
def parse_types(rows: Generator) -> Generator:
for row in rows:
yield {
"name": row["name"],
"age": int(row["age"]),
"score": float(row["score"]),
"active": row["active"] == "true",
}
def filter_adults(rows: Generator) -> Generator:
for row in rows:
if row["age"] >= 18:
yield row
def grade_students(rows: Generator) -> Generator:
for row in rows:
if row["score"] >= 90:
grade = "A"
elif row["score"] >= 80:
grade = "B"
elif row["score"] >= 70:
grade = "C"
else:
grade = "D"
yield {**row, "grade": grade}
pipeline = grade_students(
filter_adults(
parse_types(
filter_active(
parse_csv(csv_data)
)
)
)
)
for student in pipeline:
print(f"{student['name']}: {student['grade']} ({student['score']})")
# Alice: B (85.0), Diana: A (95.0), Eve: D (60.0)Warning
Be careful with infinite generators in pipelines. Always limit them with itertools.islice or risk running forever.
Expressing Intent with Built-in Functions
python
from typing import List
from functools import reduce
from itertools import groupby, chain
# Declarative "is there at least one?"
numbers = [1, 2, 3, 4, 5]
has_even = any(n % 2 == 0 for n in numbers)
print(has_even) # True
# Declarative "are all?"
all_positive = all(n > 0 for n in numbers)
print(all_positive) # True
# any/all with complex predicates
users = [
{"name": "Alice", "age": 25, "verified": True},
{"name": "Bob", "age": 17, "verified": False},
{"name": "Charlie", "age": 30, "verified": True},
]
all_adults = all(u["age"] >= 18 for u in users)
any_verified = any(u["verified"] for u in users)
print(all_adults, any_verified) # False True
# max/min with key — declarative selection
words = ["python", "java", "javascript", "rust"]
longest = max(words, key=len)
shortest = min(words, key=len)
print(longest, shortest) # "javascript" "rust"
# sum with generator
total_scores = sum(u["age"] for u in users)
print(total_scores) # 72
# zip — parallel iteration
names = ["Alice", "Bob", "Charlie"]
scores = [85, 72, 91]
pairs = list(zip(names, scores))
print(pairs) # [("Alice", 85), ("Bob", 72), ("Charlie", 91)]
# enumerate — indexed iteration
for idx, name in enumerate(names, start=1):
print(f"{idx}. {name}")
# sorted with key — declarative ordering
sorted_users = sorted(users, key=lambda u: (-u["age"], u["name"]))
print([u["name"] for u in sorted_users]) # ["Charlie", "Alice", "Bob"]itertools: Declarative Iterator Tools
python
from itertools import (
chain, accumulate,
takewhile, groupby,
product, permutations, combinations,
)
# chain — concatenate iterables
combined = list(chain([1, 2], [3, 4], [5, 6]))
print(combined) # [1, 2, 3, 4, 5, 6]
# accumulate — running total
running = list(accumulate([1, 2, 3, 4, 5]))
print(running) # [1, 3, 6, 10, 15]
# accumulate with function
running_product = list(accumulate([1, 2, 3, 4, 5], lambda a, b: a * b))
print(running_product) # [1, 2, 6, 24, 120]
# takewhile — take while predicate is true
taken = list(takewhile(lambda x: x < 10, accumulate(range(100))))
print(taken) # [0, 1, 3, 6]
# groupby — group consecutive elements
data = [("fruit", "apple"), ("fruit", "banana"), ("color", "red"), ("color", "blue")]
sorted_data = sorted(data, key=lambda x: x[0])
for key, group in groupby(sorted_data, key=lambda x: x[0]):
print(f"{key}: {[g[1] for g in group]}")
# product — cartesian product
print(list(product([1, 2], ["a", "b"])))
# [(1, "a"), (1, "b"), (2, "a"), (2, "b")]
# permutations
print(list(permutations([1, 2, 3], 2)))
# combinations
print(list(combinations([1, 2, 3], 2)))Declarative vs Imperative Side-by-Side
python
from typing import List, Dict, Any
students_data = [
{"name": "Alice", "scores": [85, 90, 78, 92], "age": 25},
{"name": "Bob", "scores": [72, 68, 75], "age": 17},
{"name": "Charlie", "scores": [91, 88, 95, 93], "age": 30},
{"name": "Diana", "scores": [95, 97], "age": 22},
]
# IMPERATIVE: Compute average scores for adults
def average_scores_imperative(students: List[Dict[str, Any]]) -> Dict[str, float]:
result = {}
for s in students:
if s["age"] >= 18:
total = 0
for score in s["scores"]:
total += score
avg = total / len(s["scores"])
result[s["name"]] = round(avg, 2)
return result
# DECLARATIVE: Same logic as comprehension
def average_scores_declarative(students: List[Dict[str, Any]]) -> Dict[str, float]:
return {
s["name"]: round(sum(s["scores"]) / len(s["scores"]), 2)
for s in students
if s["age"] >= 18
}
print(average_scores_imperative(students_data))
print(average_scores_declarative(students_data))
# IMPERATIVE: Flatten and deduplicate
def flatten_unique_imperative(lists: List[List[int]]) -> List[int]:
result = []
seen = set()
for sublist in lists:
for item in sublist:
if item not in seen:
seen.add(item)
result.append(item)
return result
# DECLARATIVE: Same logic
def flatten_unique_declarative(lists: List[List[int]]) -> List[int]:
return list(dict.fromkeys(item for sublist in lists for item in sublist))
nested = [[1, 2, 3], [2, 3, 4], [3, 4, 5]]
print(flatten_unique_imperative(nested)) # [1, 2, 3, 4, 5]
print(flatten_unique_declarative(nested)) # [1, 2, 3, 4, 5]The Walrus Operator in Comprehensions
The walrus operator (:=) lets you assign and use a value in the same expression.
python
from typing import List
# Without walrus — recalculates
def get_even_squares_bad(nums: List[int]) -> List[int]:
return [n ** 2 for n in nums if n ** 2 % 2 == 0]
# With walrus — calculates once
def get_even_squares_good(nums: List[int]) -> List[int]:
return [sq for n in nums if (sq := n ** 2) % 2 == 0]
print(get_even_squares_good([1, 2, 3, 4, 5])) # [4, 16]
# Filter with transformation reuse
def process_orders(orders: List[Dict[str, Any]]) -> List[str]:
return [
f"{item['name']}: ${item['total']:.2f}"
for order in orders
if (item := order.get("item")) and item["total"] > 100
]
orders = [
{"item": {"name": "Laptop", "total": 1200}},
{"item": {"name": "Mouse", "total": 25}},
{"item": {"name": "Monitor", "total": 350}},
]
print(process_orders(orders))Conditional Expressions for Readability
python
from typing import List, Dict, Any
# BAD: Nested conditional in comprehension (hard to read)
def grade_students_bad(students: List[Dict[str, Any]]) -> List[str]:
return [
"A" if s["score"] >= 90 else "B" if s["score"] >= 80 else "C" if s["score"] >= 70 else "D" if s["score"] >= 60 else "F"
for s in students
]
# GOOD: Helper function makes intent clear
def letter_grade(score: float) -> str:
if score >= 90:
return "A"
elif score >= 80:
return "B"
elif score >= 70:
return "C"
elif score >= 60:
return "D"
return "F"
def grade_students_good(students: List[Dict[str, Any]]) -> List[str]:
return [letter_grade(s["score"]) for s in students]
# Declarative approach with data
def grade_students_data(students: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
thresholds = [(90, "A"), (80, "B"), (70, "C"), (60, "D")]
return [
{
**s,
"grade": next(
(grade for threshold, grade in thresholds if s["score"] >= threshold),
"F"
),
}
for s in students
]
data = [{"name": "Alice", "score": 85}]
print(grade_students_good(data)) # ["B"]
print(grade_students_data(data))Declarative Data Validation
python
from typing import List, Dict, Any, Callable
# Define validation rules declaratively
class Validator:
def __init__(self, rules: List[Callable]):
self.rules = rules
def validate(self, data: Dict[str, Any]) -> List[str]:
return [
error
for rule in self.rules
if (error := rule(data))
]
def required_field(field: str) -> Callable:
def rule(data: Dict[str, Any]) -> str | None:
if field not in data or data[field] is None:
return f"{field} is required"
return None
return rule
def min_length(field: str, min_len: int) -> Callable:
def rule(data: Dict[str, Any]) -> str | None:
value = data.get(field)
if isinstance(value, str) and len(value) < min_len:
return f"{field} must be at least {min_len} characters"
return None
return rule
def is_email(field: str) -> Callable:
def rule(data: Dict[str, Any]) -> str | None:
value = data.get(field)
if isinstance(value, str) and "@" not in value:
return f"{field} must be a valid email"
return None
return rule
def range_check(field: str, min_val: float, max_val: float) -> Callable:
def rule(data: Dict[str, Any]) -> str | None:
value = data.get(field)
if isinstance(value, (int, float)) and not (min_val <= value <= max_val):
return f"{field} must be between {min_val} and {max_val}"
return None
return rule
user_validator = Validator([
required_field("name"),
required_field("email"),
min_length("name", 2),
is_email("email"),
range_check("age", 18, 120),
])
test_user = {"name": "A", "email": "invalid", "age": 150}
errors = user_validator.validate(test_user)
for err in errors:
print(f" - {err}")Comparison: Imperative vs Declarative
| Aspect | Imperative | Declarative |
|---|---|---|
| Focus | How to do it | What to achieve |
| State | Mutable variables, manual tracking | Immutable data, automatic |
| Loops | for/while with manual indexing | Comprehensions, map/filter |
| Conditionals | if/elif/else blocks | Filter predicates, conditional expressions |
| Reuse | Copy-paste or refactor | Compose small pure functions |
| Testability | Test whole block | Test each transformation |
| Brevity | Verbose | Concise |
| Learning curve | Familiar to beginners | Requires paradigm shift |
| Parallelization | Manual (locks, threads) | Often automatic (no shared state) |
Practice Exercises
-
Rewrite this imperative code using a list comprehension:
pythonresult = [] for x in range(20): if x % 3 == 0 or x % 5 == 0: result.append(x ** 2) -
Use a dict comprehension to swap keys and values in a dictionary. Handle the case where multiple keys map to the same value.
-
Write a generator function that yields the first
ntriangular numbers (T_n = n(n+1)/2). Use itertools.islice to get the first 10. -
Create a generator pipeline that: reads lines from a list, strips whitespace, filters out empty lines and comments (lines starting with #), and yields cleaned lines.
-
Use any(), all(), and generator expressions to check: (a) if any number in a list is prime, (b) if all strings in a list are palindromes.
-
Implement a simple query builder using declarative chaining (like the Validator pattern above) that builds SQL WHERE clauses from method calls.
-
Given a list of transactions, use groupby and sum to compute total sales per category in a declarative way.
-
Rewrite this function to be purely declarative using comprehensions and built-in functions (no explicit loops):
pythondef process(users): result = [] for u in users: completed = [o for o in u["orders"] if o["status"] == "completed"] if completed: total = sum(o["total"] for o in completed) result.append({"name": u["name"], "total": total, "count": len(completed)}) return sorted(result, key=lambda x: x["total"], reverse=True)
Summary
- Comprehensions (list, dict, set) are Python's most declarative feature
- Generator expressions provide lazy, memory-efficient iteration
- Generator pipelines compose lazy transformations without intermediate storage
- Built-in functions (any, all, sum, zip, enumerate) express intent directly
- itertools provides declarative tools for complex iteration patterns
- The walrus operator enables value reuse within comprehensions
- Validation rules can be expressed declaratively as composable checks
- Declarative code is more concise, testable, and focused on intent
Success
You've mastered Python's declarative patterns. By expressing intent rather than mechanics, you write code that is shorter, clearer, and less error-prone. Combine these patterns with immutability and composition for maximum benefit.
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