Section 1.1 FastAPI: A Must-Know Framework

FastAPI may not be brand new, but its swift adoption and outstanding performance make it essential for developers in 2024. This framework is optimized for quickly and efficiently creating APIs, utilizing Starlette for web functionality and Pydantic for data handling.

Example: Creating a Simple API

from fastapi import FastAPI

app = FastAPI()

@app.get("/")

def read_root():

return {"Hello": "World"}

@app.get("/items/{item_id}")

def read_item(item_id: int, q: str = None):

return {"item_id": item_id, "q": q}

This straightforward API setup is not only quick but also remarkably efficient. I believe FastAPI could easily surpass older frameworks like Flask and Django in the realm of API development.

Section 1.2 Polars: The DataFrame Library of the Future

For those immersed in data manipulation, Polars stands out as an innovative DataFrame library designed for speed and usability. Unlike pandas, Polars is constructed in Rust, leading to notable performance gains.

Example: Manipulating Data with Polars

import polars as pl

# Create a DataFrame

df = pl.DataFrame({

"name": ["Alice", "Bob", "Charlie"],

"age": [25, 30, 35],

"salary": [70000, 80000, 90000]

})

# Execute some operations

filtered_df = df.filter(pl.col("age") > 25)

print(filtered_df)

Polars not only offers increased speed but is also more memory-efficient, making it an excellent choice for large datasets. I boldly assert that by the end of 2024, Polars will become the preferred library for data manipulation, overtaking pandas.

Section 1.3 TextAttack: Empowering NLP Applications

With the increasing prevalence of NLP applications, TextAttack emerges as a formidable library designed for adversarial attacks, data enhancement, and model training in the NLP domain.

Example: Conducting an Adversarial Attack on Text Classification

from textattack.augmentation import EmbeddingAugmenter

augmenter = EmbeddingAugmenter()

sentence = "The quick brown fox jumps over the lazy dog."

augmented_sentence = augmenter.augment(sentence)

print(augmented_sentence)

TextAttack simplifies the process of creating adversarial examples and augmenting datasets. Given the growing need for resilient NLP models, I anticipate that TextAttack will become essential for every NLP engineer.

Section 1.4 Pydantic: Enhancing Data Validation

Though Pydantic has been available for a few years, its significance in data validation and settings management is increasingly vital, particularly alongside FastAPI. Pydantic leverages Python type annotations for data parsing and validation.

Example: Validating Data with Pydantic

from pydantic import BaseModel, ValidationError

from typing import Optional, List

from datetime import datetime

class User(BaseModel):

id: int

name: str

signup_ts: Optional[datetime] = None

friends: List[int] = []

try:

user = User(id='123', name='John Doe')

except ValidationError as e:

print(e)

Pydantic is revolutionary for developers tired of repetitive validation code. Its integration with FastAPI enhances its importance. A bold claim: Pydantic’s approach to type safety may accelerate the adoption of static typing in Python more than mypy.

Section 1.5 Great Expectations: Ensuring Data Quality

Data quality is critical, and Great Expectations provides a framework that combines data validation with documentation.

Example: Validating Data with Great Expectations

import great_expectations as ge

# Create a DataFrame

df = ge.dataset.PandasDataset({

"name": ["Alice", "Bob", "Charlie"],

"age": [25, 30, 35],

"salary": [70000, 80000, 90000]

})

# Define expectations

df.expect_column_values_to_be_between("age", 20, 40)

df.expect_column_values_to_not_be_null("name")

# Validate expectations

results = df.validate()

print(results)

Great Expectations not only validates your data but also provides thorough documentation. As data governance gains traction, I believe Great Expectations will become indispensable for data teams.