When it comes to working with data in a tabular form, most people reach for a spreadsheet. That’s not a bad choice: Microsoft Excel and similar programs are familiar and loaded with functionality for ...

A while ago, I was asked by a former colleague about the best way to convert Parquet files into comma-separated values (CSV) format using Python. The honest answer? It depends. And so on and so on ...

pandas is the premier library for data analysis in Python. Here are some advanced things I like to do with pandas DataFrames to take my analysis to the next level. Change the index of a DataFrame On a ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

Why write SQL queries when you can get an LLM to write the code for you? Query NFL data using querychat, a new chatbot component that works with the Shiny web framework and is compatible with R and ...

The right Python libraries can dramatically improve speed, efficiency, and maintainability in 2025 projects. Mastering a mix of data, AI, and web-focused libraries ensures adaptability across multiple ...

Have you ever found yourself wrestling with Excel formulas, wishing for a more powerful tool to handle your data? Or maybe you’ve heard the buzz about Python in Excel and wondered if it’s truly the ...

This article is adapted from an edition of our Off the Charts newsletter originally published in October 2021. Off the Charts is a weekly, subscriber-only guide to The Economist’s award-winning data ...

Data wrangling, also known as data munging, is a critical step in any data science or data analysis project. The process entails obtaining, compiling, and converting unprocessed data into a ...

The guide provides a tutorial on building an advanced artificial intelligence (AI) agent using Python and Retrieval Augmented Generation (RAG). The AI agent is capable of utilizing various tools and ...