🧠 Dataclasses: automatic creation of methods and properties

Do you hate writing monotonous __init__, __repr__ and __eq__ for each class? Dataclasses do it for you.

😩 Manual implementation is a boring and stupid task

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y
    def __repr__(self):
        return f"Point(x={self.x}, y={self.y})"
    def __eq__(self, other):
        return self.x == other.x and self.y == other.y

class User:
    def __init__(self, name, age):
        self.name = name
        self.age = age
    def __repr__(self):
        return f"User(name={self.name}, age={self.age})"
    def __eq__(self, other):
        return self.name == other.name and self.age == other.age

Problem:
This is crap. Tons of boilerplate code that's easy to break or forget to update.

✔️ Correctly (via @dataclass)

from dataclasses import dataclass

@dataclass
class Point:
    x: int
    y: int

@dataclass
class User:
    name: str
    age: int

p1 = Point(10, 20)
p2 = Point(10, 20)
u = User("Ivan", 30)

print(p1)          # Point(x=10, y=20)
print(p1 == p2)    # True
print(u)           # User(name='Ivan', age=30)

How it works:
The decorator @dataclass automatically generates methods based on type annotations.

Customizing a dataclass:

from dataclasses import dataclass, field

@dataclass(order=True, frozen=True)
class Product:
    name: str
    price: float = 0.0
    tags: list[str] = field(default_factory=list, compare=False)

    def expensive(self):
        return self.price > 1000

p1 = Product("Laptop", 1500.0)
p2 = Product("Mouse", 50.0)
print(p1 > p2)        # True (price comparison due to order=True)
p1.tags.append("tech")
# p1.name = "PC"      # Error! frozen=True makes the object immutable

inits generated by default?:

🔵 __initreprtializer with parameters
🔵 __repr_eqetty string representation
🔵 __eq__ - comparison acltllles
gth geTrue: __lt__, __le__, __gt__, __ge__

Important:
Dataclasses

are not a replacement for regular classes. Use them for data structures where standard methods are needed.

👩‍💻 @DataScience4

Creating Barcodes in Python!

Barcode generation can be easily automated using the python-barcode library.

First, install the library itself and the dependency for image generation:

pip install python-barcode pillow

The Pillow library is used to save in PNG format via ImageWriter.

Import the main module and writer for working with images:

import barcode
from barcode.writer import ImageWriter

Let's create an EAN-13 barcode. Important: exactly 12 digits are passed, and the check digit is automatically calculated and added.

ean = barcode.get(
    "ean13",
    "590123412345",
    writer=ImageWriter()
)

Save it to disk:

filename = ean.save("product_barcode")
print(filename)

As a result, the product_barcode.png file will be created in the current directory.

Generating several barcodes: a common scenario is generating a series of barcodes, for example for a list of products or database records:

codes = [
    "123456789012",
    "987654321098",
    "111222333444"
]

for value in codes:
    code_obj = barcode.get(
        "ean13",
        value,
        writer=ImageWriter()
    )
    code_obj.save(f"barcode_{value}")

Here, it's also important to comply with the format requirement: 12 digits for EAN-13, otherwise the library will throw an exception.

If you need to encode arbitrary strings (order numbers, documents), it's more convenient to use Code128:

code128 = barcode.get(
    "code128",
    "ORDER-2025-001",
    writer=ImageWriter()
)

code128.save("order_barcode")

This format is not limited to numbers and is widely used in logistics and document management.

In practice, generation is often outsourced to a function - for reuse in services or APIs:

def generate_barcode(value, filename):
    code = barcode.get(
        "code128",
        value,
        writer=ImageWriter()
    )
    return code.save(filename)

generate_barcode("INVOICE-4587", "invoice_4587")

🔥 This approach is convenient to scale and supplement with logic (validation, writer settings, saving paths in the database, etc.).

👉 @DataScience4