Efficient Data Handling in Streamlit
This post is the second part of the Master Large Datasets for Peak Performance in Streamlit post.
Efficient Data Handling in Streamlit: Optimizing Back-End Processing and UI Rendering Streamlit is a powerful framework for building interactive data apps with minimal code. However, efficiently handling large datasets remains a challenge. Without the right tools, your app can become sluggish due to excessive memory usage and slow UI rendering.
This blog post covers two essential aspects of efficient data handling in Streamlit:
Add Authentication to Streamlit Apps
Secure Your Streamlit Apps: Implementing User Authentication Streamlit empowers you to build powerful data applications quickly. But what if you need to restrict access to sensitive data or features? User authentication is the answer. This post will guide you through two popular methods for adding user authentication to your Streamlit applications: streamlit_authenticator and AWS Amplify.
Why User Authentication Matters in Streamlit User authentication adds a crucial layer of security, ensuring that only authorized users can access your application.
Master Large Datasets for Peak Performance in Streamlit
Supercharge Your Streamlit Apps: Mastering Large Datasets for Peak Performance Streamlit’s allure lies in its ability to transform Python scripts into interactive web applications with minimal effort. However, when you introduce large datasets into the mix, that simplicity can quickly turn into sluggish performance. Fear not! This comprehensive guide will equip you with the advanced techniques necessary to optimize your Streamlit apps and handle massive datasets with ease.
The Bottleneck: Understanding the Challenges Streamlit’s core mechanism—rerunning the entire script on every interaction—is both its strength and its weakness.
Integrate Google AdSense in Streamlit Apps
Monetize Your Streamlit App: Integrating Google AdSense for Seamless Advertising So you’ve built a fantastic website with Streamlit, the user-friendly framework that makes creating web apps with Python a breeze. You’re sharing insightful content and providing valuable tools, but wouldn’t it be nice to earn a little something back for your efforts? That’s where Google AdSense comes in!
Streamlit: Your One-Stop Shop for Web Apps
If you’re new to the world of Streamlit, let’s rewind a bit.
Build a Gift Recommender with LLM and Streamlit
Unwrap Inspiration: Building a Gift Recommender with LLMs and Streamlit Struggling to find the perfect gift? Drowning in a sea of online options? Worry not, friend! Let’s dive into the exciting world of building a gift recommender app using the power of Large Language Models (LLMs) and Streamlit, a Python library for creating beautiful and interactive data apps.
The Spark of an Idea:
Imagine a user-friendly web app where you choose a few simple details – recipient’s age, gender, occasion, and budget – and voila!
Streamlit SEO Best Practices
Read our previous post to learn how to deploy your Streamlit website in your custom sub domain free of charge (assuming you already own a live domain). Streamlit is a popular Python framework for building and deploying web applications. It makes it easy to create interactive UIs with data visualization and machine learning capabilities.
Although Streamlit makes website building easy, you need to know how to set your page metadata to make your pages more discoverable by search engines.