Universal Python Scraper for Ecommerce Data

🚀 I just released PostalKit — a Python package that brings the power of libpostal to Python with a clean, zero-setup developer experience. 🌍 PostalKit is a 1:1 wrapper around the libpostal C library for high-quality international address parsing, normalization, and expansion. 💡 Why I built it: Most Python integrations around libpostal can feel heavy, fragmented, or setup-intensive. I wanted something simpler: install, import, use. ✨ What it offers: • 🐍 Pure Python interface • ⬇️ Auto-download models/assets • 💻 Cross-platform support • 🔌 Direct mapping to libpostal functions • 📦 Useful for geocoding, e-commerce, logistics, CRM, search, and data cleaning 🛠️ Example use cases: • 📍 Parse messy user-entered addresses • 🌐 Normalize addresses across countries • 🚚 Improve shipping workflows • 🧹 Clean legacy databases • 🔎 Power location search systems 🔓 Open source and available now. 📦 PyPI: https://lnkd.in/dj-2beC5 💻 GitHub: https://lnkd.in/dVWrSUs7 🙏 Feedback, stars, issues, and contributions are welcome. #Python #OpenSource #PyPI #DataEngineering #Geocoding #AddressParsing #MachineLearning #Developers #Logistics #GIS

Just shipped a Python bot that monitors competitor pricing across 47 e-commerce sites. The twist? It runs headless with Playwright, but when it hits a CAPTCHA or bot detection, it automatically switches to a pool of residential proxies and adjusts its behavior patterns. Built the whole thing in FastAPI with async processing. Takes about 3 minutes to scrape all sites vs the 2+ hours it used to take manually. The hardest part wasn't the scraping logic - it was making it look human enough to avoid getting blocked. Had to add random delays, mouse movements, and even simulate typing mistakes. Deployed on AWS Lambda with CloudWatch for monitoring. Now it runs every 6 hours and sends alerts when competitors drop prices below our thresholds. What's your approach to handling bot detection in web scraping projects? #BuildInPublic #Python #Automation #WebScraping #DevLife #Playwright #FastAPI #AWS

𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗶𝗻 𝗗𝗷𝗮𝗻𝗴𝗼. 𝗪𝗵𝗶𝗰𝗵 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗗𝗼 𝗬𝗼𝘂 𝗣𝗿𝗲𝗳𝗲𝗿? Caching was one of those topics I kept putting off learning properly. 𝗜𝘁 𝗳𝗲𝗹𝘁 𝗰𝗼𝗺𝗽𝗹𝗲𝘅. 𝗧𝗵𝗲𝗻 𝗜 𝗿𝗲𝗮𝗹𝗶𝘀𝗲𝗱 𝗗𝗷𝗮𝗻𝗴𝗼 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝘀𝘂𝗿𝗽𝗿𝗶𝘀𝗶𝗻𝗴𝗹𝘆 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗮𝗯𝗹𝗲. There are two common ways to cache in Django and they solve slightly different problems. Look at the two options in the image. ➝ 𝗢𝗽𝘁𝗶𝗼𝗻 𝟭 𝘂𝘀𝗲𝘀 𝘁𝗵𝗲 @𝗰𝗮𝗰𝗵𝗲_𝗽𝗮𝗴𝗲 𝗱𝗲𝗰𝗼𝗿𝗮𝘁𝗼𝗿. You add one line above your view and the entire response gets cached for a set duration. Simple, fast to implement, and works well for public pages that rarely change. ➝ 𝗢𝗽𝘁𝗶𝗼𝗻 𝟮 𝘂𝘀𝗲𝘀 𝗰𝗮𝗰𝗵𝗲.𝗴𝗲𝘁() 𝗮𝗻𝗱 𝗰𝗮𝗰𝗵𝗲.𝘀𝗲𝘁() 𝗺𝗮𝗻𝘂𝗮𝗹𝗹𝘆. More code, but you get precise control over what gets cached, for how long, and under what key. Here is where the difference matters in practice: ➝ @𝗰𝗮𝗰𝗵𝗲_𝗽𝗮𝗴𝗲 caches the full response regardless of content. If the data changes, users still see stale results until the cache expires. ➝ Manual caching lets you cache specific querysets or computed values and invalidate them selectively when data changes. ➝ For user-specific data, @𝗰𝗮𝗰𝗵𝗲_𝗽𝗮𝗴𝗲 can serve the wrong data to the wrong user if not configured carefully. I started with @𝗰𝗮𝗰𝗵𝗲_𝗽𝗮𝗴𝗲 because it was easy to reach for. Over time I moved to manual caching for anything that needed precise invalidation. 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝗰𝗮𝗰𝗵𝗶𝗻𝗴 𝘀𝗲𝘁𝘂𝗽 𝗹𝗼𝗼𝗸 𝗹𝗶𝗸𝗲 𝗮𝗻𝗱 𝘄𝗵𝗶𝗰𝗵 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗵𝗮𝘃𝗲 𝘆𝗼𝘂 𝗳𝗼𝘂𝗻𝗱 𝗺𝗼𝗿𝗲 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗶𝗻 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? hashtag #Django #Python #BackendDevelopment #Performance #SoftwareEngineering

 Automating E-commerce Data Extraction with Python Today, I focused on the first phase of an E-commerce Market Intelligence project: building a robust data extraction pipeline. Instead of manual data entry or using static files, I developed a Python script to interface directly with a REST API. This allows for the automated retrieval of real-time product data, ensuring the analysis is based on the most current market information. By automating the 'Collection' phase, I’m now ready to focus on the 'Analysis' phase—identifying stock risks and pricing trends through SQL and Power BI. #DataAnalytics #python #APIIntegration

#Wine_Quality_Prediction_using_Machine_Learning I built a full-stack ML project that predicts whether a wine is *Good* or *Bad* based on its chemical properties.  Tech Stack:  #Frontend: React (Vite)  #Backend: Node.js (Express)  #ML Model: Python (Flask + Scikit-learn) What I did:  # Trained a Random Forest model on wine dataset  # Converted raw input data into predictions using a Flask API  # Connected React → Node → Flask for real-time prediction  # Designed a UI form to input wine parameters and display results Features:  #Real-time prediction  #Clean UI with popup result (Good / Bad )  #Full API integration Challenges I faced: #Connecting multiple servers (React, Node, Flask) #Handling data format mismatches between frontend & backend #Fixing API errors like ECONNREFUSED Outcome: Successfully built an end-to-end ML web app where users can input wine features and instantly get quality prediction. #GitHub_Repository: https://lnkd.in/givVPBfv

I'm often asked how to handle edge cases when building data layers with MongoDB and Python. Simple CRUD is great, but real-world apps need robust query patterns and clean architecture. Working in VS Code on this project, I focused on layering logic. Instead of calling the database directly from the application layer, I used a modular service pattern (like user_service.py calling db_utils.py). A few key practices I implemented: ✅ Robust Error Handling: Ensuring a clean return for cases like invalid ObjectIds, which prevents app crashes. ✅ Modular Query Logic: Abstracting queries into specific, reusable functions (e.g., get_users_by_college) makes the main logic much easier to read and test. ✅ Automated Postman-Free Testing: In my terminal, you can see I'm using curl and echo to script a "Full CRUD Test Cycle." This is a fast, reproducible way to verify APIs during development. What's your go-to pattern for structuring database interactions in your applications? Do you stick with raw queries, ORMs, or custom data access objects? Let me know in the comments! GitHub link - > https://lnkd.in/dASzkj7T #mongodb #python #development #dataservices #vscode #backend #programming #softwareengineering

𝐃𝐣𝐚𝐧𝐠𝐨 𝟏𝟎𝟏 𝐟𝐨𝐫 𝐏𝐲𝐭𝐡𝐨𝐧𝐢𝐬𝐭𝐚𝐬 🐍 | 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐐𝐮𝐞𝐫𝐲 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 As a Django application grows, database performance becomes a central topic. One of the most common bottlenecks is the N+1 Query Problem. 💡 𝐓𝐡𝐞 𝐅𝐚𝐜𝐭: By default, Django’s ORM uses "lazy loading." It only fetches related data at the moment it is accessed. While this saves memory, it can lead to an excessive number of database hits during loops. The N+1 Scenario: If you want to display a list of 50 Books and their Authors: One query fetches the 50 books. As you loop through the books to show the author's name, Django performs a new database lookup for each individual author. 👉 This results in 51 database trips for a single list. Technical Solutions: 🚀 select_related() This is used for "one-to-many" or "one-to-one" relationships. It performs an SQL JOIN in the initial query. Book.objects.select_related('author').all() Instead of many trips, Django fetches everything in one single query. 🚀 prefetch_related() This is used for "many-to-many" or reverse relationships. It performs a separate lookup for the related objects and joins the data in Python. This effectively reduces hundreds of queries down to two. 🔍 How to identify it: Tools like django-debug-toolbar help visualize how many queries are fired per request. If you see the same SQL pattern repeating multiple times, it’s a clear indicator that the ORM needs optimization. 𝐓𝐡𝐞 𝐁𝐨𝐭𝐭𝐨𝐦 𝐋𝐢𝐧𝐞: Database "round-trips" are expensive. Using these tools ensures that your application remains performant and scalable, regardless of how much data you are handling. #Python #Django #WebDevelopment #Database #SoftwareEngineering

Django's only() and defer() methods are often overlooked, yet they are essential for optimizing memory usage when fetching data from the database. Every field retrieved from the database consumes memory, and this can become significant with large models. Consider the following examples: - Fetching all fields when only two are needed: ```python users = User.objects.all() ``` - Instead, fetch only the necessary fields: ```python users = User.objects.only('id', 'email') ``` - Alternatively, defer the heavy fields that are not immediately required: ```python users = User.objects.defer('bio', 'profile_picture') ``` For instance, with a User model that includes a TextField for bio and an ImageField for profile picture, fetching 10,000 users for an email report can lead to significant memory savings. Using only('id', 'email') reduced memory usage by 60% for that query alone. When to use which method: - Use only() when you know exactly which fields you need. - Use defer() when you want to retrieve everything except a few heavy fields. This small change can lead to a big impact at scale. 🚀 #Django #Python #DjangoORM #BackendPerformance #PythonDev #BackendDevelopment #HappyLearning

🚀 Day 19: Models & Database in Django As I dive deeper into Django, I explored how data is structured and managed using Models. 👉 In Django, a Model defines the structure of your database. It acts as a bridge between your application and the database. 🔹 What is a Model? A model is a Python class that represents a database table. Each attribute in the class corresponds to a column in the table. 💡 Example: from django.db import models class Student(models.Model):     name = models.CharField(max_length=100)     age = models.IntegerField() 🔹 Key Concepts: ✔ Fields → Define data types (CharField, IntegerField, etc.) ✔ Migrations → Apply changes to the database ✔ ORM (Object Relational Mapping) → Interact with DB using Python instead of SQL 🔹 Basic Commands: python manage.py makemigrations python manage.py migrate 📌 Why it matters? ✔ Simplifies database operations ✔ Eliminates the need to write raw SQL ✔ Makes applications scalable and maintainable Django’s ORM is one of its most powerful features for backend development. 💡 Good developers don’t just store data they structure it efficiently. 📈 Step by step, building real-world backend expertise. #Django #Python #Database #BackendDevelopment #ORM #WebDevelopment #LearningJourney #FullStack

Day-120,121 📘 Python Full Stack Journey – Django Forms & User Input Handling Today I learned how to handle user input in Django using models and forms — an important step toward building interactive and data-driven applications. 🚀 🎯 What I learned today: 🗄️ Model Creation (Contact Form) Created a Contact model to store user data: Name Email Phone number Applied migrations and registered the model in Django Admin for easy data management 📝 Django ModelForm Created a form using Django’s built-in ModelForm: class BookingContact(forms.ModelForm): class Meta: model = Contact fields = '__all__' Learned how Django automatically generates form fields from models 🌐 Displaying Forms in Templates Rendered forms in HTML using: {{ form }} {{ form.as_p }} for structured layout 📩 Form Submission (POST Method) Used POST method for secure data submission Added {% csrf_token %} for protection Handled form submission in views.py: if request.method == 'POST': form = BookingContact(request.POST) if form.is_valid(): form.save() 🎨 Custom Form Styling Styled individual form fields manually using labels and inputs Learned how to design forms for better user experience This session helped me understand how Django manages forms, validation, and database storage seamlessly — a key step in building real-world web applications. Excited to keep building more interactive features! 💻✨ #Django #Python #FullStackDevelopment #WebDevelopment #Backend #Forms #Database #CodingJourney #LearningToCode #Upskilling #ContinuousLearning