Pandas vs Polars vs DuckDB: Which Library Should You Choose? π€π
pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows ππ. Polars focus on fast, memory-efficient DataFrame processing β‘πΎ, while DuckDB brings a SQL-first approach for querying local files and embedded analytics ποΈπ.
Each tool fits a different kind of local data workflow π οΈ. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases ππ.
More: https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/ π
#DataScience #Pandas #Polars #DuckDB #Python #Analytics
pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows ππ. Polars focus on fast, memory-efficient DataFrame processing β‘πΎ, while DuckDB brings a SQL-first approach for querying local files and embedded analytics ποΈπ.
Each tool fits a different kind of local data workflow π οΈ. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases ππ.
More: https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/ π
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Forwarded from Learn Python Coding
Data validation with Pydantic! πβ¨
In the early stages of development, data validation usually doesn't cause problems. In many Python projects, validation initially looks simple:
But then come email, JSON from APIs, query parameters, nested objects, configs, nullable fields, and type conversion. At some point, the code turns into a set of if/else and manual checks.
For such tasks, Pydantic is often used. Installation:
Create a model:
Now the data is validated automatically:
The result:
30
<class 'int'>
Pydantic will automatically convert the string "30" to an int. If you pass an incorrect value, you'll get a ValidationError:
This is especially convenient when working with APIs, JSON, query parameters, and incoming data from outside.
A common production case is checking email:
If the email is invalid, Pydantic will throw a ValidationError. You can set default values:
And allow None:
This field becomes optional. A practical example is processing an API response:
The types will be automatically converted. For nested model structures, you can combine:
The nested object will also be validated. Serialization in Pydantic v2:
Pydantic is actively used in FastAPI, ETL, microservices, data pipelines, and API clients.
For working with environment variables in Pydantic v2, a separate package is usually used:
It's important to understand: Pydantic is not an ORM and does not replace business logic. Its task is to validate data, convert types, and describe schemas.
π₯ Pydantic significantly reduces the amount of manual data validation and makes processing incoming structures more predictable.
#Python #Pydantic #DataValidation #FastAPI #Coding #DevOps
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In the early stages of development, data validation usually doesn't cause problems. In many Python projects, validation initially looks simple:
if not isinstance(age, int):
raise ValueError("age must be an int")
But then come email, JSON from APIs, query parameters, nested objects, configs, nullable fields, and type conversion. At some point, the code turns into a set of if/else and manual checks.
For such tasks, Pydantic is often used. Installation:
pip install pydantic
pip install "pydantic[email]"
Create a model:
from pydantic import BaseModel
class User(BaseModel):
name: str
age: int
Now the data is validated automatically:
user = User(
name="Alex",
age="30"
)
print(user.age)
print(type(user.age))
The result:
30
<class 'int'>
Pydantic will automatically convert the string "30" to an int. If you pass an incorrect value, you'll get a ValidationError:
User(
name="Alex",
age="test"
)
This is especially convenient when working with APIs, JSON, query parameters, and incoming data from outside.
A common production case is checking email:
from pydantic import BaseModel, EmailStr
class User(BaseModel):
email: EmailStr
User(email="[email protected]")
If the email is invalid, Pydantic will throw a ValidationError. You can set default values:
from pydantic import BaseModel
class Config(BaseModel):
host: str = "localhost"
port: int = 5432
And allow None:
from pydantic import BaseModel
class User(BaseModel):
nickname: str | None = None
This field becomes optional. A practical example is processing an API response:
from pydantic import BaseModel
class Product(BaseModel):
id: int
title: str
price: float
data = {
"id": "1",
"title": "Keyboard",
"price": "99.5"
}
product = Product(**data)
print(product)
The types will be automatically converted. For nested model structures, you can combine:
from pydantic import BaseModel
class Address(BaseModel):
city: str
zip_code: str
class User(BaseModel):
name: str
address: Address
user = User(
name="Alex",
address={
"city": "Berlin",
"zip_code": "10115"
}
)
print(user)
The nested object will also be validated. Serialization in Pydantic v2:
print(user.model_dump())
print(user.model_dump_json())
Pydantic is actively used in FastAPI, ETL, microservices, data pipelines, and API clients.
For working with environment variables in Pydantic v2, a separate package is usually used:
pip install pydantic-settings
It's important to understand: Pydantic is not an ORM and does not replace business logic. Its task is to validate data, convert types, and describe schemas.
π₯ Pydantic significantly reduces the amount of manual data validation and makes processing incoming structures more predictable.
#Python #Pydantic #DataValidation #FastAPI #Coding #DevOps
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I came across a great find from Vizuara β a series of 43 lectures that truly delivers on its promise: showing how to build a large language model from scratch. π§ β¨
Most people use ChatGPT.
But only a few actually understand how it works under the hood. βοΈ
This playlist step by step breaks down all the key concepts without overloading with complex explanations.
π What you will learn:
β The architecture of Transformer ποΈ
β The internal structure of GPT
β Tokenization and BPE π§©
β Attention mechanisms π
β The process of training an LLM π
β Full implementations in Python π
β Suitable for:
β’ ML engineers
β’ AI enthusiasts
β’ Developers entering the GenAI field
β’ Anyone who is tired of explaining AI as a "black box" π΅οΈ
If you really want to understand what lies at the heart of models like ChatGPT, Claude, and Gemini β this material is worth watching. π
π Link to the playlist:
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Forwarded from Machine Learning with Python
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A Collection of Machine Learning Libraries for Python π€
A large repository containing over 900 libraries and frameworks for machine learning. π
All projects are sorted by quality and popularity, which helps you quickly find the best tools for working with AI and ML. βοΈ
Repo: https://github.com/ml-tooling/best-of-ml-python?tab=readme-ov-file#vector-similarity-search-ann
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A large repository containing over 900 libraries and frameworks for machine learning. π
All projects are sorted by quality and popularity, which helps you quickly find the best tools for working with AI and ML. βοΈ
Repo: https://github.com/ml-tooling/best-of-ml-python?tab=readme-ov-file#vector-similarity-search-ann
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