🐍 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.