\n
from flask import Flask, jsonify app = Flask(__name__) @app.route('\/api\/users', methods=['GET']) def get_users(): users = [{\"id\": 1, \"name\": \"Alice\"}, {\"id\": 2, \"name\": \"Bob\"}] return jsonify(users) if __name__ == '__main__': app.run(debug=True)<\/pre>\n<\/div>\nExplanation<\/strong>:<\/p>\n\n- Purpose<\/strong>: Exposes an
\/api\/users<\/code> endpoint returning JSON data.<\/li>\n- Code<\/strong>: Lightweight Flask app with a single route.<\/li>\n
- Output<\/strong>: Access
http:\/\/127.0.0.1:5000\/api\/users<\/code> to see [{\"id\": 1, \"name\": \"Alice\"}, {\"id\": 2, \"name\": \"Bob\"}]<\/code>.<\/li>\n- Use Case<\/strong>: Ideal for quick APIs or prototyping.<\/li>\n<\/ul>\n
Example 3: FastAPI Async API<\/h3>\n
Create an asynchronous API endpoint.<\/p>\n
\n
from fastapi import FastAPI from pydantic import BaseModel import uvicorn app = FastAPI() class Item(BaseModel): name: str price: float @app.post(\"\/items\/\") async def create_item(item: Item): return {\"name\": item.name, \"price\": item.price} if __name__ == '__main__': uvicorn.run(app, host=\"127.0.0.1\", port=8000)<\/pre>\n<\/div>\nExplanation<\/strong>:<\/p>\n\n- Purpose<\/strong>: Creates a POST endpoint to receive item data.<\/li>\n
- Code<\/strong>: Uses Pydantic for data validation and async\/await for performance.<\/li>\n
- Output<\/strong>: Send a POST request\u00a0
http:\/\/127.0.0.1:8000\/items\/<\/code> with JSON {\"name\": \"Laptop\", \"price\": 999.99}<\/code> to get the same data back.<\/li>\n- Use Case<\/strong>: High-performance APIs for microservices.<\/li>\n<\/ul>\n
Example 4: Pandas Data Analysis<\/h3>\n
Analyze a dataset of sales.<\/p>\n
\n
import pandas as pd # Sample sales data data = pd.DataFrame({ 'product': ['Laptop', 'Phone', 'Tablet', 'Laptop'], 'price': [1000, 600, 300, 1200], 'quantity': [5, 10, 8, 3] }) # Calculate total revenue per product data['revenue'] = data['price'] * data['quantity'] summary = data.groupby('product')['revenue'].sum().reset_index() print(summary)<\/pre>\n<\/div>\nOutput<\/strong>:<\/p>\n\n
product revenue 0 Laptop 6500 1 Phone 6000 2 Tablet 2400<\/pre>\n<\/div>\nExplanation<\/strong>:<\/p>\n\n- Purpose<\/strong>: Aggregates sales data to compute total revenue by product.<\/li>\n
- Code<\/strong>: Uses Pandas’ DataFrame for easy manipulation and grouping.<\/li>\n
- Use Case<\/strong>: Data exploration, reporting, or preprocessing.<\/li>\n<\/ul>\n
Example 5: scikit-learn Classification<\/h3>\n
Build a simple classifier.<\/p>\n
\n
from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score # Load data iris = load_iris() X, y = iris.data, iris.target # Split data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Train model clf = RandomForestClassifier(random_state=42) clf.fit(X_train, y_train) # Evaluate y_pred = clf.predict(X_test) print(f\"Accuracy: {accuracy_score(y_test, y_pred):.2f}\")<\/pre>\n<\/div>\nOutput<\/strong>:<\/p>\n\n
Accuracy: 1.00<\/pre>\n<\/div>\nExplanation<\/strong>:<\/p>\n\n- Purpose<\/strong>: Classifies Iris flowers using a Random Forest model.<\/li>\n
- Code<\/strong>: Leverages scikit-learn’s consistent API for training and evaluation.<\/li>\n
- Use Case<\/strong>: Predictive modeling, customer segmentation.<\/li>\n<\/ul>\n
Example 6: TensorFlow Neural Network<\/h3>\n
Build a simple neural network.<\/p>\n
\n
import tensorflow as tf from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split # Synthetic data X, y = make_classification(n_samples=1000, n_features=20, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Build model model = tf.keras.Sequential([ tf.keras.layers.Dense(64, activation='relu', input_shape=(20,)), tf.keras.layers.Dense(32, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid') ]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) model.fit(X_train, y_train, epochs=5, batch_size=32, verbose=0) # Evaluate loss, accuracy = model.evaluate(X_test, y_test, verbose=0) print(f\"Accuracy: {accuracy:.2f}\")<\/pre>\n<\/div>\nOutput<\/strong>:<\/p>\n\n
Accuracy: 0.92<\/pre>\n<\/div>\nExplanation<\/strong>:<\/p>\n\n- Purpose<\/strong>: Trains a neural network for binary classification.<\/li>\n
- Code<\/strong>: Uses TensorFlow’s Keras API for simplicity.<\/li>\n
- Use Case<\/strong>: Deep learning for complex tasks like image or text analysis.<\/li>\n<\/ul>\n
4. Comparison of Frameworks<\/h2>\n
\n\n\n| Framework<\/th>\n | Category<\/th>\n | Strengths<\/th>\n | Weaknesses<\/th>\n | Best For<\/th>\n<\/tr>\n<\/thead>\n |
\n\n| Django<\/td>\n | Web Development<\/td>\n | Full-stack, secure, scalable<\/td>\n | Steeper learning curve<\/td>\n | Enterprise apps, CMS<\/td>\n<\/tr>\n |
\n| Flask<\/td>\n | Web Development<\/td>\n | Lightweight, flexible<\/td>\n | Limited built-in features<\/td>\n | Small apps, APIs<\/td>\n<\/tr>\n |
\n| FastAPI<\/td>\n | Web Development<\/td>\n | High performance, async, modern<\/td>\n | Less mature ecosystem<\/td>\n | APIs, microservices<\/td>\n<\/tr>\n |
\n| Pandas<\/td>\n | Data Science<\/td>\n | Intuitive, powerful DataFrames<\/td>\n | Memory-intensive for big data<\/td>\n | Data analysis, preprocessing<\/td>\n<\/tr>\n |
\n| NumPy<\/td>\n | Data Science<\/td>\n | Fast array operations<\/td>\n | Limited to numerical data<\/td>\n | Scientific computing<\/td>\n<\/tr>\n |
\n| Dask<\/td>\n | Data Science<\/td>\n | Scales Pandas\/NumPy, distributed<\/td>\n | Complex setup for clusters<\/td>\n | Big data processing<\/td>\n<\/tr>\n |
\n| scikit-learn<\/td>\n | Machine Learning<\/td>\n | Easy-to-use, robust algorithms<\/td>\n | Not suited for deep learning<\/td>\n | Traditional ML, prototyping<\/td>\n<\/tr>\n |
\n| TensorFlow<\/td>\n | Machine Learning<\/td>\n | Scalable, production-ready<\/td>\n | Steeper learning curve<\/td>\n | Deep learning, production<\/td>\n<\/tr>\n |
\n| PyTorch<\/td>\n | Machine Learning<\/td>\n | Flexible, research-friendly<\/td>\n | Less focus on deployment<\/td>\n | Research, dynamic models<\/td>\n<\/tr>\n |
\n| Scapy<\/td>\n | Network Security<\/td>\n | Powerful packet manipulation<\/td>\n | Requires networking knowledge<\/td>\n | Security auditing, packet analysis<\/td>\n<\/tr>\n |
\n| Paramiko<\/td>\n | Network Security<\/td>\n | Secure SSH\/SFTP, easy to use<\/td>\n | Limited to SSH-based tasks<\/td>\n | Remote administration<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n5. Best Practices<\/h2>\n\n- Choose the Right Framework<\/strong>:\n
\n- Use Django for large, feature-rich apps; Flask\/FastAPI for APIs; Pandas\/NumPy for data analysis; TensorFlow\/PyTorch for deep learning.<\/li>\n<\/ul>\n<\/li>\n
- Modular Code<\/strong>:\n
\n- Structure projects with clear separation of concerns (e.g., Django’s MTV model).<\/li>\n<\/ul>\n<\/li>\n
- Security<\/strong>:\n
|