Uncover the hidden pitfalls of Excel regression and learn why Python is the key to unlocking clean, efficient data analysis.

Each tool serves different needs, from simplicity to speed and SQL-based analytics workflows. Performance differences matter most, with Polars and DuckDB outperforming Pandas on large datasets. Modern ...

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 ...

Already using NumPy, Pandas, and Scikit-learn? Here are seven more powerful data wrangling tools that deserve a place in your toolkit. Python’s rich ecosystem of data science tools is a big draw for ...

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 ...

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 ...