🐍 Python Tip #71: The operator.attrgetter() Function

The attrgetter() function from Python's operator module lets you retrieve attributes from objects.

It is especially useful when sorting custom objects.

---

Importing attrgetter

from operator import attrgetter

---

Basic Example

from operator import attrgetter

class Student:
    def __init__(self, name, score):
        self.name = name
        self.score = score

student = Student("Yehya", 95)

print(attrgetter("name")(student))
print(attrgetter("score")(student))

Output:

Yehya
95

---

Sorting Objects

from operator import attrgetter

class Student:
    def __init__(self, name, score):
        self.name = name
        self.score = score

students = [
    Student("Ali", 82),
    Student("Sara", 96),
    Student("John", 74),
    Student("Mariam", 89)
]

students.sort(key=attrgetter("score"))

for student in students:
    print(student.name, student.score)

Output:

John 74
Ali 82
Mariam 89
Sara 96

---

Sorting in Reverse

students.sort(
    key=attrgetter("score"),
    reverse=True
)

for student in students:
    print(student.name, student.score)

Output:

Sara 96
Mariam 89
Ali 82
John 74

---

Getting Multiple Attributes

from operator import attrgetter

get_info = attrgetter("name", "score")

print(get_info(student))

Output:

('Yehya', 95)

---

Why use attrgetter()?

✓ Retrieves object attributes efficiently
✓ Makes sorting custom objects simple
✓ Works well with sorted() and .sort()
✓ Useful in object-oriented programming and data processing

---

Challenge:

Create a Product class with:

name
price

Create at least four products.

Then use:

attrgetter("price")

to sort them from cheapest to most expensive.

🚀 Python Tip #72: The operator.methodcaller() Function

The methodcaller() function from Python's operator module creates a callable that calls a specific method on an object.

It is useful when working with functions like map().

---

Importing methodcaller

from operator import methodcaller

---

Basic Example

from operator import methodcaller

upper = methodcaller("upper")

print(upper("python"))

Output:

PYTHON

This is similar to:

"python".upper()

---

Using methodcaller() with map()

from operator import methodcaller

words = ["python", "java", "c++"]

result = list(map(methodcaller("upper"), words))

print(result)

Output:

['PYTHON', 'JAVA', 'C++']

---

Passing Arguments to a Method

methodcaller() can also pass arguments to the method.

from operator import methodcaller

words = ["python", "programming", "coding"]

result = list(
    map(methodcaller("replace", "o", "0"), words)
)

print(result)

Output:

['pyth0n', 'pr0gramming', 'c0ding']

---

Using methodcaller() with Custom Classes

from operator import methodcaller

class Student:
    def __init__(self, name):
        self.name = name

    def greet(self):
        return f"Hello, {self.name}!"

students = [
    Student("Ali"),
    Student("Sara"),
    Student("John")
]

greet = methodcaller("greet")

for message in map(greet, students):
    print(message)

Output:

Hello, Ali!
Hello, Sara!
Hello, John!

---

Why use methodcaller()?

✓ Calls methods dynamically
✓ Works well with map()
✓ Can pass arguments to methods
✓ Useful in functional programming and data processing

---

Challenge:

Given:

words = [
    "python",
    "programming",
    "developer"
]

Use:

```python
methodcaller()

with `map()` to convert every word to uppercase.

Expected Output:

python
['PYTHON', 'PROGRAMMING', 'DEVELOPER']
`

🐍 Python Tip #73: The itertools Module

The itertools module provides powerful tools for working with iterators.

It can help you create combinations, permutations, repeated values, and efficient loops.

---

Importing itertools

import itertools

---

Creating Combinations

combinations() generates all possible combinations of a specific length.

from itertools import combinations

numbers = [1, 2, 3, 4]

result = list(combinations(numbers, 2))

print(result)

Output:

[(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]

The order does not matter in combinations.

---

Creating Permutations

permutations() generates arrangements where order matters.

from itertools import permutations

numbers = [1, 2, 3]

result = list(permutations(numbers, 2))

print(result)

Output:

[
    (1, 2),
    (1, 3),
    (2, 1),
    (2, 3),
    (3, 1),
    (3, 2)
]

Notice that (1, 2) and (2, 1) are different.

---

Repeating Values with repeat()

from itertools import repeat

for value in repeat("Python", 3):
    print(value)

Output:

Python
Python
Python

---

Why use itertools?

✓ Provides efficient iterator-based tools
✓ Useful for combinations and permutations
✓ Helps avoid writing complex loops manually
✓ Commonly used in algorithms, data processing, and mathematical problems

---

Challenge:

Given:

students = ["Ali", "Sara", "John", "Mariam"]

Use:

```python
combinations()

to generate every possible **pair of students**.

Expected Output:

python
('Ali', 'Sara')
('Ali', 'John')
('Ali', 'Mariam')
('Sara', 'John')
('Sara', 'Mariam')
('John', 'Mariam')
`

🚀 Python Tip #74: itertools.chain()

The chain() function from the itertools module lets you combine multiple iterables into one continuous iterator.

It's useful when you want to loop through several lists without creating a new combined list.

---

Importing chain

from itertools import chain

---

Basic Example

from itertools import chain

numbers1 = [1, 2, 3]
numbers2 = [4, 5, 6]

for number in chain(numbers1, numbers2):
    print(number)

Output:

1
2
3
4
5
6

---

Combining Lists

You can also convert the result into a list:

from itertools import chain

list1 = ["A", "B"]
list2 = ["C", "D"]
list3 = ["E", "F"]

result = list(chain(list1, list2, list3))

print(result)

Output:

['A', 'B', 'C', 'D', 'E', 'F']

---

Why use chain()?

✓ Combines multiple iterables efficiently
✓ Avoids unnecessary nested loops
✓ Doesn't create a new combined list unless you explicitly convert it
✓ Useful for data processing and working with large collections

---

Challenge:

Given:

group1 = ["Ali", "Sara"]
group2 = ["John", "Mariam"]
group3 = ["Yusuf", "Aisha"]

Use:

```python
chain()

to loop through all six students and print their names one by one.

Expected Output:

text
Ali
Sara
John
Mariam
Yusuf
Aisha
`

🐍 Python Tip #75: itertools.islice()

The islice() function from Python's itertools module allows you to select a specific portion of an iterable without creating the entire sequence in memory.

It's especially useful when working with large datasets, generators, and streams.

---

Importing islice

from itertools import islice

---

Basic Example

from itertools import islice

numbers = range(10)

result = list(islice(numbers, 5))

print(result)

Output:

[0, 1, 2, 3, 4]

This takes the first 5 items.

---

Using Start and Stop

from itertools import islice

numbers = range(10)

result = list(islice(numbers, 2, 7))

print(result)

Output:

[2, 3, 4, 5, 6]

The syntax is similar to:

numbers[2:7]

---

Using a Step

from itertools import islice

numbers = range(20)

result = list(islice(numbers, 0, 20, 3))

print(result)

Output:

[0, 3, 6, 9, 12, 15, 18]

The third argument specifies the step.

---

Working with a Generator

from itertools import islice

def numbers():
    for i in range(1000000):
        yield i

result = list(islice(numbers(), 10))

print(result)

Output:

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

Only the required values are consumed from the generator.

---

Why use islice()?

✓ Efficiently processes part of an iterable
✓ Doesn't require slicing support
✓ Works with generators and iterators
✓ Useful for large datasets and streams

---

Challenge:

Create a generator that produces numbers from 1 to 1000.

Use:

islice()

to extract only the numbers from position 50 through 59.

Expected Output:

[51, 52, 53, 54, 55, 56, 57, 58, 59, 60]

🐍 Python Tip #76: Regular Expressions with re

Python's re module lets you search, match, extract, and replace text using regular expressions (regex).

Regex is extremely useful for text processing, validation, and searching patterns.

---

Importing re

import re

---

Searching for a Pattern

import re

text = "I am learning Python."

match = re.search(r"Python", text)

if match:
    print("Python was found!")

Output:

Python was found!

---

Finding All Matches

Use findall() to get every occurrence of a pattern.

import re

text = "Python is powerful. Python is popular."

matches = re.findall(r"Python", text)

print(matches)

Output:

['Python', 'Python']

---

Extracting Numbers

import re

text = "I have 25 books and 3 notebooks."

numbers = re.findall(r"\d+", text)

print(numbers)

Output:

['25', '3']

Here, `\d+ means "one or more digits."

---

Replacing Text

Use sub() to replace matching text.

``python
import re

text = "Python is difficult."

result = re.sub(r"difficult", "fun", text)

print(result)

Output:

text
Python is fun.

---

Simple Email Pattern

python
import re

email = "student@example.com"

pattern = r"^[\w.-]+@[\w.-]+\.\w+$"

if re.fullmatch(pattern, email):
print("Valid email")
else:
print("Invalid email")

Output:

text
Valid email

---

Useful Regex Patterns

text
\d → digit
\w → letter, digit, or underscore
\s → whitespace
+ → one or more
* → zero or more
? → zero or one
^ → beginning of string
$ → end of string
. → any character

---

Why use `re`?

✓ Search text using patterns  
✓ Extract useful information  
✓ Validate input formats  
✓ Replace matching text  
✓ Useful in automation, data processing, web development, and text analysis

---

Challenge:

Given:

python
text = "Contact us at 123-456-7890 or 987-654-3210."

Use `re.findall()` to extract all phone numbers.

Expected Output:

python
['123-456-7890', '987-654-3210']
`

🐍 Python Tip #77: yield from

The yield from statement lets a generator delegate part of its work to another iterable or generator.

It is especially useful when you want to combine generators without writing an extra loop.

---

Basic Example:

def numbers():
    yield from [1, 2, 3, 4]

for number in numbers():
    print(number)

Output:

1
2
3
4

This is equivalent to:

def numbers():
    for number in [1, 2, 3, 4]:
        yield number

---

Combining Generators:

def first():
    yield 1
    yield 2
    yield 3

def second():
    yield 4
    yield 5
    yield 6

def all_numbers():
    yield from first()
    yield from second()

print(list(all_numbers()))

Output:

[1, 2, 3, 4, 5, 6]

Instead of manually looping through each generator, yield from delegates the iteration automatically.

---

Using Multiple Iterables:

def combined():
    yield from ["Python", "Java"]
    yield from ["C++", "JavaScript"]

print(list(combined()))

Output:

['Python', 'Java', 'C++', 'JavaScript']

---

Why use yield from?

✓ Makes generator code shorter
✓ Combines multiple generators easily
✓ Avoids unnecessary nested loops
✓ Works efficiently with iterators
✓ Useful for data pipelines and recursive generators

---

Challenge:

Create three generators:

def fruits():
    yield "Apple"
    yield "Banana"

def vegetables():
    yield "Carrot"
    yield "Potato"

def foods():
    # Use yield from here
    pass

Make `foods() produce all four items.

Expected Output:

``python
['Apple', 'Banana', 'Carrot', 'Potato']
`

🐍 Python Tip #78: itertools.groupby()

The groupby() function from Python's itertools module groups consecutive elements that have the same key.

It's useful for organizing and analyzing sequences of data.

---

Importing groupby

from itertools import groupby

---

Basic Example

from itertools import groupby

numbers = [1, 1, 2, 2, 2, 3, 3]

for key, group in groupby(numbers):
    print(key, list(group))

Output:

1 [1, 1]
2 [2, 2, 2]
3 [3, 3]

---

Important: groupby() Groups Consecutive Values

from itertools import groupby

numbers = [1, 2, 1, 2, 1]

for key, group in groupby(numbers):
    print(key, list(group))

Output:

1 [1]
2 [2]
1 [1]
2 [2]
1 [1]

It does not automatically combine all identical values.

---

Grouping Objects by a Key

from itertools import groupby

students = [
    {"name": "Ali", "grade": "A"},
    {"name": "Sara", "grade": "A"},
    {"name": "John", "grade": "B"},
    {"name": "Mariam", "grade": "B"}
]

for grade, group in groupby(students, key=lambda student: student["grade"]):
    print(grade, list(group))

Output:

A [{'name': 'Ali', 'grade': 'A'}, {'name': 'Sara', 'grade': 'A'}]
B [{'name': 'John', 'grade': 'B'}, {'name': 'Mariam', 'grade': 'B'}]

---

Sorting Before Grouping

If equal values are not next to each other, sort the data first.

from itertools import groupby

numbers = [2, 1, 2, 3, 1, 3, 2]

numbers.sort()

for key, group in groupby(numbers):
    print(key, list(group))

Output:

1 [1, 1]
2 [2, 2, 2]
3 [3, 3]

---

Why use groupby()?

✓ Groups related consecutive data
✓ Works efficiently with iterators
✓ Useful for data processing and organization
✓ Can group complex objects using a key function

---

Challenge:

Given:

students = [
    {"name": "Ali", "class": "A"},
    {"name": "Sara", "class": "A"},
    {"name": "John", "class": "B"},
    {"name": "Mariam", "class": "B"},
    {"name": "Yusuf", "class": "C"}
]

Use `groupby() to group the students by their "class"`.

Print each class and the students belonging to it.

🐍 Python Tip #79: functools.partial()

The partial() function from Python's functools module lets you create a new function with some arguments already filled in.

This is useful when you want to reuse a function with certain values fixed.

---

Importing partial

from functools import partial

---

Basic Example

from functools import partial

def multiply(a, b):
    return a * b

double = partial(multiply, 2)

print(double(5))
print(double(10))

Output:

10
20

Here, 2 is automatically supplied as the first argument.

So:

double(5)

is effectively:

multiply(2, 5)

---

Creating Multiple Specialized Functions

from functools import partial

def power(base, exponent):
    return base ** exponent

square = partial(power, exponent=2)
cube = partial(power, exponent=3)

print(square(5))
print(cube(5))

Output:

25
125

---

Using partial() with map()

from functools import partial

def add(number, amount):
    return number + amount

add_10 = partial(add, amount=10)

numbers = [1, 2, 3, 4]

result = list(map(add_10, numbers))

print(result)

Output:

[11, 12, 13, 14]

---

Why use partial()?

✓ Reuses existing functions
✓ Pre-fills function arguments
✓ Creates specialized versions of functions
✓ Useful with callbacks, map(), event handlers, and APIs

---

Challenge:

Create a function:

def calculate(price, tax):
    return price + (price * tax)

Then use partial() to create:

calculate_15_percent

where the tax is fixed at:

0.15

Test it with:

100

Expected output:

115.0

🐍 Python Tip #80: The weakref Module

Python's weakref module lets you create references to objects without preventing those objects from being garbage-collected.

This can be useful when building caches, object registries, and memory-sensitive applications.

---

Importing weakref

import weakref

---

Basic Example

import weakref

class User:
def init(self, name):
self.name = name

user = User("Yehya")

reference = weakref.ref(user)

print(reference().name)

Output:

Yehya

reference() gives you access to the original object while it still exists.

---

What Happens When the Object Is Deleted?

import weakref

class User:
def init(self, name):
self.name = name

user = User("Yehya")

reference = weakref.ref(user)

print(reference())

del user

print(reference())

Output:

<main.User object at ...>
None

After user is deleted and no strong references remain, the object can be garbage-collected.

The weak reference doesn't keep it alive.

---

Using a Weak Reference Callback

You can run a function when the referenced object is about to be finalized.

import weakref

class User:
pass

def object_deleted(reference):
print("Object was deleted.")

user = User()

reference = weakref.ref(
user,
object_deleted
)

del user

Possible output:

Object was deleted.

---

Why use weakref?

✓ Helps avoid unnecessary memory retention
✓ Useful for caches and object registries
✓ Allows objects to be garbage-collected normally
✓ Useful in advanced Python applications and frameworks

---

Challenge:

Create a class named:

Product

Then:

1. Create a Product object.
2. Create a weak reference to it.
3. Print the object through the weak reference.
4. Delete the original object.
5. Check the weak reference again.

What do you expect it to return after the object is deleted?

🐍 Python Tip #81: collections.deque

Ever needed a list where you're constantly adding and removing items from both ends?

That's where deque comes in.

deque means "double-ended queue" and is designed for fast operations at both the beginning and the end of a sequence.

---

Importing deque

from collections import deque

---

Basic Example

from collections import deque

queue = deque(["Ali", "Sara", "John"])

queue.append("Mariam")
queue.appendleft("Yusuf")

print(queue)

Output:

deque(['Yusuf', 'Ali', 'Sara', 'John', 'Mariam'])

---

Removing Items

from collections import deque

queue = deque(["Ali", "Sara", "John"])

first = queue.popleft()
last = queue.pop()

print(first)
print(last)
print(queue)

Output:

Ali
John
deque(['Sara'])

popleft() removes from the left side.

pop() removes from the right side.

---

Why not just use a normal list?

You can use:

numbers.pop(0)

But removing the first item from a large list can be slower because the remaining elements have to be shifted.

With deque:

numbers.popleft()

Removing from the left is designed to be efficient.

---

Useful Methods

append(x) → add to the right
appendleft(x) → add to the left
pop() → remove from the right
popleft() → remove from the left
extend(items) → add multiple items to the right
rotate(n) → rotate the elements

---

Real Example: A Simple Queue

from collections import deque

tasks = deque()

tasks.append("Download file")
tasks.append("Process data")
tasks.append("Send report")

while tasks:
task = tasks.popleft()
print("Processing:", task)

Output:

Processing: Download file
Processing: Process data
Processing: Send report

This is a simple example of a FIFO queue:

First In → First Out

---

Why use deque?

✓ Fast adding/removing from both ends
✓ Great for queues and task processing
✓ Useful for sliding windows and buffers
✓ Better suited than lists for frequent left-side operations
✓ Part of Python's standard library

---

Challenge:

Create a deque containing:

["A", "B", "C"]

Then:

1. Add "D" to the right.
2. Add "Z" to the left.
3. Remove the first item.
4. Remove the last item.
5. Print the final deque.

What do you get?

🐍 Python Tip #82: The bisect Module

Ever wanted to keep a list sorted without manually searching for where a new value should go?

Python's bisect module can help.

It uses binary search to find the correct position in an already-sorted list.

---

Importing bisect

import bisect

---

Finding Where to Insert

import bisect

numbers = [10, 20, 30, 40]

position = bisect.bisect(numbers, 25)

print(position)

Output:

2

The result 2 means 25 should be inserted at index 2.

---

Inserting Automatically

You can use insort() to find the position AND insert the value.

import bisect

numbers = [10, 20, 30, 40]

bisect.insort(numbers, 25)

print(numbers)

Output:

[10, 20, 25, 30, 40]

No need to manually search for the correct position.

---

Another Example

import bisect

scores = [45, 60, 72, 85, 95]

bisect.insort(scores, 80)

print(scores)

Output:

[45, 60, 72, 80, 85, 95]

The list stays sorted automatically.

---

bisect_left() vs bisect_right()

When duplicate values exist, these functions help decide where to insert.

import bisect

numbers = [10, 20, 20, 20, 30]

print(bisect.bisect_left(numbers, 20))
print(bisect.bisect_right(numbers, 20))

Output:

1
4

bisect_left() gives the position before the existing 20s.

bisect_right() gives the position after them.

---

Why use bisect?

✓ Keeps sorted lists organized
✓ Uses binary search to find positions
✓ Useful for ranking and score systems
✓ Great for maintaining ordered data
✓ Built into Python's standard library

---

Challenge:

Start with:

numbers = [10, 20, 30, 40, 50]

Use bisect.insort() to insert:

25
35
45

Then print the final list.

Expected result:

[10, 20, 25, 30, 35, 40, 45, 50]

🐍 Python Tip #83: heapq

Need to quickly find the smallest or largest items in a collection?

Python's heapq module gives you a heap-based priority queue.

It's especially useful when you repeatedly need the smallest or largest values without sorting the entire list every time.

---

Importing heapq

import heapq

---

Creating a Min-Heap

A normal list can be turned into a heap using heapify().

import heapq

numbers = [7, 2, 9, 1, 5]

heapq.heapify(numbers)

print(numbers)

The smallest value is always at index 0.

print(numbers[0])

Output:

1

---

Removing the Smallest Item

Use heappop():

import heapq

numbers = [7, 2, 9, 1, 5]

heapq.heapify(numbers)

smallest = heapq.heappop(numbers)

print(smallest)
print(numbers)

Output:

1
[2, 5, 9, 7]

The heap structure isn't necessarily completely sorted.

It only guarantees that the smallest item is at the front.

---

Adding an Item

Use heappush():

import heapq

numbers = [2, 5, 7]

heapq.heapify(numbers)

heapq.heappush(numbers, 1)

print(numbers)

Output:

[1, 2, 7, 5]

---

Getting the Smallest Items

You can use nsmallest() when you only need a few of the smallest values.

import heapq

numbers = [15, 3, 27, 8, 12, 1]

result = heapq.nsmallest(3, numbers)

print(result)

Output:

[1, 3, 8]

You can also use nlargest():

result = heapq.nlargest(3, numbers)

print(result)

Output:

[27, 15, 12]

---

Real Example: Priority Tasks

import heapq

tasks = []

heapq.heappush(tasks, (2, "Send email"))
heapq.heappush(tasks, (1, "Fix critical bug"))
heapq.heappush(tasks, (3, "Update documentation"))

while tasks:
priority, task = heapq.heappop(tasks)
print(priority, task)

Output:

1 Fix critical bug
2 Send email
3 Update documentation

The smaller priority number gets processed first.

---

Why use heapq?

✓ Useful for priority queues
✓ Efficiently handles the smallest items
✓ Can find the largest/smallest N values
✓ Useful in scheduling and algorithms
✓ Common in real-world data processing

---

Challenge:

Given:

numbers = [42, 7, 19, 3, 25, 11]

Use heapq to:

1. Turn the list into a heap.
2. Remove the smallest value.
3. Add 5.
4. Print the smallest 3 values.

Try solving it without using sorted().

🐍 Python Tip #84: collections.defaultdict

Ever needed a dictionary where missing keys should automatically get a default value?

That's exactly what defaultdict is useful for.

It's part of Python's collections module and can make grouping and counting data much cleaner.

---

Importing defaultdict

from collections import defaultdict

---

Basic Example

from collections import defaultdict

scores = defaultdict(int)

scores["Ali"] += 10
scores["Sara"] += 20

print(scores)

Output:

defaultdict(<class 'int'>, {'Ali': 10, 'Sara': 20})

With a normal dictionary, trying to modify a key that doesn't exist would require creating it first.

defaultdict(int) automatically starts missing values at 0.

---

Counting Items

One useful application is counting occurrences:

from collections import defaultdict

fruits = ["apple", "banana", "apple", "orange", "banana", "apple"]

counts = defaultdict(int)

for fruit in fruits:
counts[fruit] += 1

print(dict(counts))

Output:

{'apple': 3, 'banana': 2, 'orange': 1}

---

Creating Lists Automatically

You can also use list as the default factory.

from collections import defaultdict

students = defaultdict(list)

students["Class A"].append("Ali")
students["Class A"].append("Sara")
students["Class B"].append("John")

print(dict(students))

Output:

{'Class A': ['Ali', 'Sara'], 'Class B': ['John']}

You don't need to manually create an empty list for every new class.

---

Another Example: Grouping Data

from collections import defaultdict

products = [
("Laptop", "Electronics"),
("Phone", "Electronics"),
("Desk", "Furniture"),
("Chair", "Furniture")
]

categories = defaultdict(list)

for product, category in products:
categories[category].append(product)

print(dict(categories))

Output:

{
'Electronics': ['Laptop', 'Phone'],
'Furniture': ['Desk', 'Chair']
}

---

Why use defaultdict?

✓ Automatically handles missing keys
✓ Great for counting
✓ Useful for grouping data
✓ Makes dictionary-based code cleaner
✓ Part of Python's standard library

---

Challenge:

Given:

words = ["python", "java", "python", "c", "java", "python"]

Use defaultdict(int) to count how many times each programming language appears.

Expected result:

{'python': 3, 'java': 2, 'c': 1}

🐍 Python Tip #85: functools.reduce()

Sometimes you have a list of values and want to repeatedly combine them into one final result.

That's where reduce() comes in.

It applies a function to the first two items, then uses the result with the next item, and keeps going until only one final value remains.

---

Importing reduce

from functools import reduce

---

Basic Example

from functools import reduce

numbers = [1, 2, 3, 4, 5]

total = reduce(lambda a, b: a + b, numbers)

print(total)

Output:

15

What's happening?

1 + 2 → 3
3 + 3 → 6
6 + 4 → 10
10 + 5 → 15

---

Multiplying Values

from functools import reduce

numbers = [2, 3, 4]

result = reduce(lambda a, b: a * b, numbers)

print(result)

Output:

24

Because:

2 × 3 × 4 = 24

---

Using a Normal Function

You don't have to use a lambda.

from functools import reduce

def add(a, b):
return a + b

numbers = [10, 20, 30, 40]

result = reduce(add, numbers)

print(result)

Output:

100

---

Using an Initial Value

You can provide a starting value as the third argument.

from functools import reduce

numbers = [1, 2, 3]

result = reduce(
lambda a, b: a + b,
numbers,
10
)

print(result)

Output:

16

The calculation starts with:

10 + 1 + 2 + 3 = 16

---

When Should You Use reduce()?

It's useful when a sequence needs to be repeatedly combined into one value.

Examples include:

✓ Calculating products
✓ Combining values
✓ Building custom aggregation logic
✓ Functional programming
✓ Processing a sequence into one final result

For simple sums, though, sum() is usually clearer.

---

Challenge:

Given:

numbers = [2, 4, 6, 8]

Use reduce() to multiply all the numbers together.

Expected output:

384

Can you do it without using math.prod()?

🐍 Python Tip #86: functools.wraps()

When you create decorators in Python, there's a small problem that can easily be overlooked: the decorator can replace the original function's name and documentation.

functools.wraps() fixes that by preserving important information about the original function.

---

Importing wraps

from functools import wraps

---

The Problem Without wraps()

def my_decorator(func):
def wrapper():
print("Before function")
func()
return wrapper

@my_decorator
def greet():
"""Say hello to the user."""
print("Hello!")

print(greet.name)
print(greet.doc)

Output:

wrapper
None

The original function's metadata has been replaced by the wrapper's metadata.

---

Using wraps()

from functools import wraps

def my_decorator(func):
@wraps(func)
def wrapper():
print("Before function")
func()
return wrapper

@my_decorator
def greet():
"""Say hello to the user."""
print("Hello!")

print(greet.name)
print(greet.doc)

Output:

greet
Say hello to the user.

Now Python knows that the wrapped function is still greet.

---

Why Is This Useful?

Decorators are common in real Python projects.

They're used for things like:

✓ Logging
✓ Authentication
✓ Timing functions
✓ Caching
✓ Validation
✓ Permission checks
✓ Framework features

Using @wraps(func) makes your decorators behave more like the original functions.

---

A More Practical Example

from functools import wraps
import time

def timer(func):
@wraps(func)
def wrapper(*args, **kwargs):
start = time.perf_counter()

result = func(*args, **kwargs)

end = time.perf_counter()

print(f"{func.name} took {end - start:.6f} seconds")

return result

return wrapper

@timer
def calculate():
return sum(range(100000))

print(calculate())

The decorator measures the function's execution time while @wraps() preserves the original function's metadata.

---

Quick Rule

When writing your own decorators, a good habit is:

@wraps(func)

Put it directly above the wrapper function.

---

Challenge:

Create a decorator called logger that:

1. Prints "Function started" before running a function.
2. Runs the original function.
3. Prints "Function finished" afterward.
4. Uses @wraps(func).

Then decorate a function called hello() and check its __name__.

It should still print:

hello

🐍 Python Tip #87: collections.ChainMap

Ever needed to work with multiple dictionaries as if they were one?

ChainMap from the collections module lets you combine multiple dictionaries into a single view without actually merging or copying them.

---

Importing ChainMap

from collections import ChainMap

---

Basic Example

from collections import ChainMap

defaults = {
"theme": "light",
"language": "English"
}

user_settings = {
"theme": "dark"
}

settings = ChainMap(user_settings, defaults)

print(settings["theme"])
print(settings["language"])

Output:

dark
English

Python checks the dictionaries from left to right.

So if a key exists in user_settings, that value is used first.

---

Why Is This Useful?

Imagine an application has:

1. User settings
2. Project settings
3. Default settings

Instead of copying everything into one dictionary, you can use:

settings = ChainMap(
user_settings,
project_settings,
defaults
)

If the user has a custom value, it takes priority.

If not, Python checks the next dictionary.

---

Adding a New Dictionary

You can add another mapping with new_child():

from collections import ChainMap

defaults = {"color": "blue"}
user = {"name": "Ali"}

settings = ChainMap(user, defaults)

temporary = settings.new_child(
{"color": "red"}
)

print(temporary["color"])

Output:

red

The new dictionary gets the highest priority.

---

Updating Values

If you change a value:

settings["name"] = "Sara"

the change is made in the first dictionary in the chain.

ChainMap doesn't merge everything into one new dictionary.

It keeps references to the original mappings.

---

Real-World Example

from collections import ChainMap

system_config = {
"debug": False,
"language": "English"
}

app_config = {
"debug": True
}

config = ChainMap(app_config, system_config)

print(config["debug"])
print(config["language"])

Output:

True
English

This pattern can be useful for configuration systems where different levels of settings override each other.

---

Why use ChainMap?

✓ Combines multiple dictionaries without copying them
✓ Useful for configuration systems
✓ Supports priority-based lookups
✓ Keeps the original dictionaries separate
✓ Useful when working with layered settings

---

Challenge:

Create three dictionaries:

defaults = {"theme": "light", "font": "Arial"}
user = {"theme": "dark"}
temporary = {"font": "Courier"}

Use ChainMap so that:

1. temporary has the highest priority.
2. user comes next.
3. defaults is the fallback.

Then print the values of:

theme
font

Expected output:

dark
Courier

🐍 Python Tip #88: contextvars

Ever needed a variable whose value is local to the current task or execution context?

Python's contextvars module is especially useful in asynchronous applications where multiple tasks can be running at the same time.

---

Importing contextvars

import contextvars

---

Creating a Context Variable

import contextvars

user = contextvars.ContextVar("user")

user.set("Yehya")

print(user.get())

Output:

Yehya

ContextVar stores a value that belongs to the current execution context.

---

Using a Default Value

import contextvars

language = contextvars.ContextVar(
"language",
default="English"
)

print(language.get())

Output:

English

If you haven't assigned a value yet, the default is returned.

---

Changing the Value

import contextvars

username = contextvars.ContextVar(
"username",
default="Guest"
)

print(username.get())

username.set("Yehya")

print(username.get())

Output:

Guest
Yehya

---

Using ContextVar in Functions

import contextvars

current_user = contextvars.ContextVar(
"current_user",
default="Guest"
)

def show_user():
print("Current user:", current_user.get())

current_user.set("Ali")

show_user()

current_user.set("Sara")

show_user()

Output:

Current user: Ali
Current user: Sara

---

Why Is This Useful?

ContextVar is commonly useful in:

✓ Async applications
✓ Web servers
✓ Request-specific data
✓ Logging context
✓ User/session information
✓ Code where multiple tasks need separate values

For example, a web application might store a request ID in a ContextVar so different parts of the application can access it without passing the ID through every function.

---

Important Difference

A normal global variable is shared:

current_user = "Ali"

A ContextVar is designed so different execution contexts can have different values.

This becomes especially useful with asyncio and other concurrent code.

---

Challenge:

Create a ContextVar called:

current_user

Give it a default value of:

"Guest"

Then:

1. Print the default value.
2. Change it to "Yehya".
3. Create a function that prints the current user.
4. Call the function.

Expected output:

Guest
Yehya

🐍 Python Tip #89: types.SimpleNamespace

Sometimes you have a few related values and want to access them with dot notation instead of dictionary brackets.

SimpleNamespace from Python's types module gives you a simple object for storing attributes.

---

Importing SimpleNamespace

from types import SimpleNamespace

---

Basic Example

from types import SimpleNamespace

user = SimpleNamespace(
name="Yehya",
age=16,
language="Python"
)

print(user.name)
print(user.age)
print(user.language)

Output:

Yehya
16
Python

Instead of:

user["name"]

you can use:

user.name

---

Changing Values

The attributes can be changed normally:

user.age = 17

print(user.age)

Output:

17

You can also add new attributes:

user.country = "Ethiopia"

print(user.country)

Output:

Ethiopia

---

Creating One From a Dictionary

You can also convert dictionary data into a namespace:

from types import SimpleNamespace

data = {
"name": "Sara",
"score": 95
}

student = SimpleNamespace(**data)

print(student.name)
print(student.score)

Output:

Sara
95

---

Converting Back to a Dictionary

Use vars():

data = vars(student)

print(data)

Output:

{'name': 'Sara', 'score': 95}

---

When Is It Useful?

SimpleNamespace can be handy for:

✓ Small data containers
✓ Configuration objects
✓ Temporary structured data
✓ Making dictionary data easier to access
✓ Quick prototypes

It's not a replacement for dataclass when you need a more structured data model.

---

Real Example

from types import SimpleNamespace

server = SimpleNamespace(
host="localhost",
port=8000,
debug=True
)

print(f"Server: {server.host}:{server.port}")
print("Debug:", server.debug)

Output:

Server: localhost:8000
Debug: True

---

Challenge:

Create a SimpleNamespace called student with:

name = "Yehya"
grade = 11
subject = "Python"

Then:

1. Print the student's name.
2. Change the grade to 12.
3. Add a new attribute called score.
4. Print all the information.

Can you do it without using a dictionary?

🐍 Python Tip #90: functools.cache()

If a function keeps receiving the same arguments and doing the same expensive calculation again and again, caching can save a lot of work.

Python's functools.cache() automatically remembers previous results and reuses them when the same arguments appear again.

---

Importing cache

from functools import cache

---

Basic Example

from functools import cache

@cache
def square(number):
print("Calculating...")
return number ** 2

print(square(5))
print(square(5))
print(square(10))

Output:

Calculating...
25
25
Calculating...
100

Notice that "Calculating..." only appears once for 5.

The second square(5) gets the stored result instead of running the function again.

---

A More Useful Example

from functools import cache

@cache
def fibonacci(n):
if n < 2:
return n

return fibonacci(n - 1) + fibonacci(n - 2)

print(fibonacci(10))

Output:

55

Without caching, recursive Fibonacci calculations repeat many of the same calculations.

With @cache, previously calculated results are reused.

---

Checking the Cache

You can inspect the cache with:

fibonacci.cache_info()

For example, you might see:

CacheInfo(hits=8, misses=11, maxsize=None, currsize=11)

hits = results retrieved from the cache.

misses = calculations that had to actually run.

currsize = number of stored results.

---

Clearing the Cache

If you want to remove all stored results:

fibonacci.cache_clear()

After that, the function will calculate the results again when called.

---

Important Note

@cache is basically an unlimited cache.

That means stored results can continue growing as new arguments are used.

If you need a limited cache size, functools.lru_cache() is usually more appropriate.

---

Why use cache()?

✓ Avoids repeating expensive calculations
✓ Extremely useful with recursive functions
✓ Simple to add with @cache
✓ Can make repeated function calls much faster
✓ Part of Python's standard library

---

Challenge:

Create a cached function:

@cache
def factorial(n):

It should calculate:

5! = 120

Then call:

factorial(5)
factorial(5)
factorial(6)

Finally, use:

factorial.cache_info()

and check how many cache hits you get.