Section 1.1: Flask

Flask is a minimalist web framework that facilitates the rapid creation of web applications. Its simplicity allows you to select your desired libraries and tools for various project components. To kickstart your Flask journey, consider this straightforward application example:

from flask import Flask

app = Flask(__name__)

@app.route('/')

def hello_world():

return 'Hello, World!'

if __name__ == '__main__':

app.run()

Section 1.2: Django

Django is a comprehensive web framework that comes equipped with numerous built-in features such as user authentication and database migrations. Adopting the "batteries-included" philosophy, it serves as an excellent option for developing complex web applications.

To create a new Django project and app, you can use the following commands:

django-admin startproject myproject

cd myproject

python manage.py startapp myapp

Section 1.3: TensorFlow

TensorFlow, an open-source machine learning framework developed by Google, excels in deep learning endeavors and enables efficient neural network creation and training. Here’s a basic example of setting up a neural network with TensorFlow:

import tensorflow as tf

model = tf.keras.Sequential([

tf.keras.layers.Dense(64, activation='relu', input_shape=(784,)),

tf.keras.layers.Dense(10, activation='softmax')

])

Section 1.4: Pandas

Pandas is an indispensable library for data manipulation, offering user-friendly data structures and analytical tools. It is essential for any data-centric project. You can load a CSV file using Pandas like this:

import pandas as pd

data = pd.read_csv('data.csv')

Section 1.5: Flask-RESTful

Flask-RESTful is an extension designed for Flask that streamlines REST API development. It allows you to easily create routes and manage HTTP methods.

from flask import Flask

from flask_restful import Api, Resource

app = Flask(__name__)

api = Api(app)

class HelloWorld(Resource):

def get(self):

return {'message': 'Hello, World!'}

api.add_resource(HelloWorld, '/')

Section 1.6: Celery

Celery is a framework for handling asynchronous tasks, enabling you to delegate lengthy processes to be executed in the background. This significantly enhances your application's responsiveness.

from celery import Celery

app = Celery('myapp', broker='pyamqp://guest@localhost//')

@app.task

def add(x, y):

return x + y

Section 1.7: FastAPI

FastAPI is a cutting-edge, high-performance web framework tailored for building APIs with Python. It is designed for speed and includes automatic documentation generation.

from fastapi import FastAPI

app = FastAPI()

@app.get("/")

def read_root():

return {"message": "Hello, World"}

Section 1.8: SQLAlchemy

SQLAlchemy serves as an SQL toolkit and Object-Relational Mapping (ORM) library for Python, simplifying database interactions and offering extensive customization options.

from sqlalchemy import create_engine, Column, Integer, String

from sqlalchemy.ext.declarative import declarative_base

from sqlalchemy.orm import sessionmaker

engine = create_engine('sqlite:///mydatabase.db')

Base = declarative_base()

class User(Base):

__tablename__ = 'users'

id = Column(Integer, primary_key=True)

name = Column(String)

Session = sessionmaker(bind=engine)

session = Session()

Section 1.9: Dash by Plotly

Dash is a Python framework for developing analytical web applications, particularly effective for crafting interactive data dashboards.

import dash

import dash_core_components as dcc

import dash_html_components as html

app = dash.Dash(__name__)

app.layout = html.Div([

html.H1('Hello, Dash!'),

dcc.Graph(id='example-graph', figure={'data': [{'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'SF'}]})

])

if __name__ == '__main__':

app.run_server(debug=True)

Section 1.10: PyQT

PyQt provides a set of Python bindings for the Qt application framework, making it ideal for creating desktop applications with graphical user interfaces.

import sys

from PyQt5.QtWidgets import QApplication, QLabel

app = QApplication(sys.argv)

label = QLabel('Hello, PyQt!')

label.show()

sys.exit(app.exec_())

These frameworks represent just a fraction of the myriad tools accessible to Python developers. Gaining proficiency in these frameworks can significantly enhance your programming productivity.