Python

Python

155 posts
Top 10 Streamlit Widgets and How to Use Them
Academy Membership StreamlitPython

Top 10 Streamlit Widgets and How to Use Them

📘 Introduction Creating interactive web apps with Streamlit is incredibly easy — even if you're not a front-end developer. Once you've mastered the basics, Streamlit’s widget system becomes your playground. These widgets let users interact with your data and models in real time. In this post, you&...

Getting Started with Streamlit: Build Your First Data App
Academy Membership StreamlitPython

Getting Started with Streamlit: Build Your First Data App

📘 Introduction Data scientists and analysts often struggle to share their work interactively. Whether it's model results or beautiful visualizations, the gap between code and a usable interface can be frustrating. That's where Streamlit comes in. Streamlit is a powerful Python framework that turns your data scripts...

Connect FastAPI to PostgreSQL with SQLModel and Pydantic Settings
Academy Membership FastAPIPostgreSQL

Connect FastAPI to PostgreSQL with SQLModel and Pydantic Settings

📘Introduction FastAPI is a modern, high-performance web framework for building APIs with Python. In real-world applications, you'll often need to connect to a relational database like PostgreSQL to store and retrieve data. But building that connection securely and cleanly—especially across development, staging, and production environments—requires a...

Manage Settings and Environment Variables in FastAPI
Academy Membership FastAPIPython

Manage Settings and Environment Variables in FastAPI

Introduction FastAPI is a modern, fast (high-performance) web framework for building APIs with Python. When building real-world applications, you’ll often need to manage sensitive information like API keys, database URLs, or feature toggles that should vary across environments (development, testing, production). Using environment variables and Pydantic’s BaseSettings is...

PySpark - Get statistical Properties of a DataFrame
Academy Membership PySparkPython

PySpark - Get statistical Properties of a DataFrame

Introduction When working with PySpark DataFrames, understanding the statistical properties of your data is crucial for data exploration and preprocessing. PySpark provides the describe() and summary() functions to generate useful summary statistics. In this tutorial, we’ll explore how to use both functions to get insights into our dataset. 📥 Import...

You’ve successfully subscribed to Deep Learning Nerds | The ultimate Learning Platform for AI and Data Science
Welcome back! You’ve successfully signed in.
Great! You’ve successfully signed up.
Success! Your email is updated.
Your link has expired
Success! Check your email for magic link to sign-in.