Excel is my database, Python is my brain.

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 ...

Data work in 2026 asks for more than chart building. Professionals are expected to clean data, query databases, explain trends, and present findings clearly across business, finance, product, and ...

This article is not about ethics, privacy, security, ownership, or corporate governance — I am going to circumvent all of this here by using some made-up data relating to supermarket sales: Here, I ...

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 ...

tcapy is a Python library for doing transaction cost analysis (TCA), essentially finding the cost of your trading activity. Across the industry many financial firms and corporates trading within ...

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 ...

Your browser does not support the audio element. Pandas is a Python library used for data analysis and manipulation on labeled datasets. The core mission of the ...

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 ...