Introduction The Hugging Face Inference API makes it easy to send prompts to large language models (LLMs) hosted on the Hugging Face Hub. By combining this with Gradio, you can quickly build interactive chatbots and demos with a simple, web-based user interface—without worrying about backend frameworks. In this tutorial,...
Introduction The Hugging Face Inference API makes it easy to send prompts to large language models (LLMs) hosted on the Hugging Face Hub. By combining this with FastAPI—a modern Python web framework—you can build scalable, production-ready APIs that serve LLM-powered responses to your applications. In this tutorial, you’...
Introduction The Hugging Face Inference API is a powerful service that lets you interact with large language models (LLMs) hosted on the Hugging Face Hub. Whether you’re building chatbots, summarization tools, or other AI-powered applications, the Inference API makes it simple to send prompts to an LLM and receive...
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...
Introduction dlt (data load tool) is a powerful Python package that simplifies data ingestion and helps you build efficient data pipelines. In the Extract, Load, Transform (ELT) process, dlt is particularly suited for the Extract (E) and Load (L) stages. In this tutorial, we'll guide you through the...
Introduction When working with PySpark DataFrames, handling different data types correctly is essential for data preprocessing. Mismatched or incorrect data types can lead to errors in Spark operations such as filtering, aggregations, and machine learning workflows. In this tutorial, we’ll explore how to convert column data types in a...
Introduction When working with data in Python, two of the most popular tools available are Pandas and PySpark. Both offer powerful data manipulation capabilities but are designed for different use cases. Pandas is well-suited for small to moderately large datasets, offering an intuitive API for data analysis. PySpark, on the...
Introduction When working with PySpark DataFrames, handling missing or empty values is a common task in data preprocessing. In many cases, empty strings ("") should be treated as null values for better compatibility with Spark operations, such as filtering, aggregations, and machine learning workflows. In this tutorial, we’ll...
Introduction When working with data in PySpark, you might often encounter scenarios where a single column contains multiple pieces of information, such as a combination of names, categories, or attributes. In such cases, it is essential to split these values into separate columns for better data organization and analysis. In...