✨ unpacking | Python Glossary ✨
📖 Passing multiple values at once by expanding an iterable.
🏷️ #Python
📖 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
📖 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
f-strings are convenient for aligning columns without additional tools. This makes the output readable in the CLI and logs:
Debug expressions (Python 3.8+):
Specifiers
Specifiers support width and padding, for example 08d for zeros. This is convenient for reports and IDs:
You can access dictionaries and immediately calculate metrics, for example
🔥 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
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']}=», items={data['items']}")
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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/
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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
📖 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
📖 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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✨ wildcard import | Python Glossary ✨
📖 An import uses the star syntax to pull many names into your current namespace at once.
🏷️ #Python
📖 An import uses the star syntax to pull many names into your current namespace at once.
🏷️ #Python
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I. Core Functions: Fully Automated "Lead Generation - Interaction - Conversion"
Precise Lead Generation and Human-like Communication: Ant AI is trained on over 20 million real social chat records, enabling it to autonomously identify target customers and build trust through natural conversation, requiring no human intervention.
High Conversion Rate Across Multiple Scenarios: Ant AI intelligently recommends high-conversion-rate products based on chat content, guiding customers to complete purchases through platforms such as iFood, Shopee, and Amazon. It also supports other transaction scenarios such as movie ticket purchases and utility bill payments.
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✨ cProfile | Python Standard Library ✨
📖 Provides a way to measure where time is being spent in your application.
🏷️ #Python
📖 Provides a way to measure where time is being spent in your application.
🏷️ #Python
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✨ 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
📖 Test GeoPandas basics for reading, mapping, projecting, and spatial joins to handle geospatial data confidently.
🏷️ #intermediate #data-science
Forwarded from Machine Learning with Python
Do you see yourself as a programmer, researcher, or engineer?
Anonymous Poll
49%
Programmer
20%
Researcher
32%
Engineer
Do you hate writing monotonous
__init__, __repr__ and __eq__ for each class? Dataclasses do it for you.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.
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, fieldinits generated by default?:
@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
are not a replacement for regular classes. Use them for data structures where standard methods are needed.🔵 __initreprtializer with parameters🔵 __repr_eqetty string representation🔵 __eq__ - comparison acltllles
gth geTrue: __lt__, __le__, __gt__, __ge__
Important:
Dataclasses
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