Code With Python
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This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
Admin: @HusseinSheikho || @Hussein_Sheikho
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Anaconda Navigator | Python Tools

📖 A desktop graphical interface included with the Anaconda Distribution.

🏷️ #Python
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unpacking | Python Glossary

📖 Passing multiple values at once by expanding an iterable.

🏷️ #Python
Quiz: How to Integrate Local LLMs With Ollama and Python

📖 Check your understanding of using Ollama with Python to run local LLMs, generate text, chat, and call tools for private, offline apps.

🏷️ #intermediate #ai #tools
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Working with f-strings: more possibilities than it seems!

f-strings often replace .format() in everyday code, but their capabilities are not always fully utilized. They support formatting, function calls, working with data structures, and convenient debugging (from 3.8+).

f-strings are convenient for aligning columns without additional tools. This makes the output readable in the CLI and logs:
rows = [
    ("id", "name", "role"),
    (1, "Ivan", "admin"),
    (2, "Olga", "editor"),
]

for r in rows:
    print(f"{r[0]:<5} {r[1]:<10} {r[2]:<10}")


Debug expressions (Python 3.8+): {x=> displays the name and value of the variable, which speeds up debugging. Supports formatting of calculations:
x = 12
y = 7
print(f"{x=} {y=} {x*y=} x/y={x/y:.3f}")


Specifiers !r, !a: !r - repr(), !a - ascii() for unambiguous logs. Eliminates ambiguities in the output of objects:
path = "/var/data/config.yaml"
print(f"{path!r} {path!a}")  # repr and ascii()


Specifiers support width and padding, for example 08d for zeros. This is convenient for reports and IDs:
n = 42
print(f"{n:08d}")  # → #00000042


You can access dictionaries and immediately calculate metrics, for example len():
data = {"user": "Ivan", "items": [1, 2, 3]}
print(f"{data['user&#39]}=», items={data['items&#39]}")
print(f"len(data['items&#39])={len(data['items&#39])}")


🔥 f-strings are a cool tool for formatting, logging, and debugging, if you apply them taking into account the version of Python and the context of the output.

🚪 @DataScience4
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Forwarded from PyData Careers
Python Clean Code: Stop Writing Bad Code — Lessons from Uncle Bob

Are you tired of writing messy and unorganized code that leads to frustration and bugs? You can transform your code from a confusing mess into something crystal clear with a few simple changes. In this article, we'll explore key principles from the book "Clean Code" by Robert C. Martin, also known as Uncle Bob, and apply them to Python. Whether you're a web developer, software engineer, data analyst, or data scientist, these principles will help you write clean, readable, and maintainable Python code.

Read: https://habr.com/en/articles/841820/

https://t.iss.one/CodeProgrammer 🧠
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relative import | Python Glossary

📖 Import modules from the same package or parent packages using leading dots.

🏷️ #Python
GeoPandas Basics: Maps, Projections, and Spatial Joins

📖 Dive into GeoPandas with this tutorial covering data loading, mapping, CRS concepts, projections, and spatial joins for intuitive analysis.

🏷️ #intermediate #data-science
This channels is for Programmers, Coders, Software Engineers.

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transitive dependency | Python Glossary

📖 An indirect requirement of your project.

🏷️ #Python
wildcard import | Python Glossary

📖 An import uses the star syntax to pull many names into your current namespace at once.

🏷️ #Python
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Ant AI Automated Sales Robot is an intelligent robot focused on automating lead generation and sales conversion. Its core function simulates human conversation, achieving end-to-end business conversion and easily generating revenue without requiring significant time investment.

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If you are interested, please join my Telegram group for more information and leave a message: https://t.iss.one/+lVKtdaI5vcQ1ZDA1
6
cProfile | Python Standard Library

📖 Provides a way to measure where time is being spent in your application.

🏷️ #Python
3
Quiz: GeoPandas Basics: Maps, Projections, and Spatial Joins

📖 Test GeoPandas basics for reading, mapping, projecting, and spatial joins to handle geospatial data confidently.

🏷️ #intermediate #data-science
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🧠 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
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