📘 Introduction When working with data spread across multiple CSV files, combining them into one unified dataset can save time and simplify your workflow. In this guide, you’ll learn how to merge multiple CSV files into a single Pandas DataFrame using just a few lines of Python. ✅ Prerequisites Before you...
📘 Introduction Working with structured data is a core part of any data engineering or analytics workflow. DuckDB, often called the SQLite for analytics, makes it incredibly easy to query local data files—including JSON—without requiring a complex setup. In this tutorial, you’ll learn how to load JSON data...
📘 Introduction JSON is one of the most widely used data formats for APIs, configurations, storage, and modern applications. Converting CSV to JSON in Python is incredibly simple using Pandas. In this tutorial, we will walk through the full process: creating a sample CSV file, loading it into a Pandas DataFrame,...
📘 Introduction In this tutorial, you’ll learn how to load data from a Parquet file into a DuckDB database using Python. DuckDB’s native Parquet support makes it fast and efficient to work with columnar data, making it ideal for analytics, ETL pipelines, and Python data projects. You’ll see...
📘 Introduction In modern data workflows, Parquet is a popular columnar storage format for efficient data storage and faster analytics. Converting CSV to Parquet in Python is straightforward using Pandas and PyArrow. In this tutorial, we will walk you through the complete process: from creating a sample CSV file, reading it...
📘 Introduction In this tutorial, you will learn how to insert data from a CSV file into a DuckDB database using Python. DuckDB is a powerful in-process analytical database that makes working with structured data fast, simple, and efficient—perfect for data projects, notebooks, and local analytics. ✅ Prerequisites Before you begin,...
📘 Introduction Choosing the right framework can dramatically shape the experience of building and deploying generative AI applications. Streamlit and Gradio are two of the most popular tools for rapidly creating AI demos, prototypes, and interactive interfaces — but they shine in different scenarios. In this guide, we’ll break down when...
📘 Introduction DuckDB has quickly become a favorite tool among data engineers and analysts because of its speed, simplicity, and ability to run analytical SQL queries directly within Python. Whether you’re prototyping data pipelines, running local analytics, or managing lightweight data storage, knowing how to insert data into DuckDB is...
📘 Introduction Renaming columns is one of the most common transformations you’ll perform when cleaning or standardizing data in PySpark. Whether you’re aligning tables from different systems, preparing data for machine learning, or simply making column names more readable, updating many column names at once can quickly become tedious...