data manipulation
data manipulation on Beyond Market Intelligence: a running collection of 5 stories we have gathered and hand-picked because they are worth your time. Every post here touches on data manipulation in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around data manipulation, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Finding most recent dates in carious columns of date information
Analyzing game data can quickly become complex, even for seasoned Magic: The Gathering players. If you're seeking to identify your most recently played decks from a spreadsheet with multiple date columns, you’re facing a common challenge. Our AI-native spreadsheet technology empowers you to transform this task from daunting to discoverable. To achieve this, explore utilizing advanced sorting and filtering capabilities, enabling you to rank dates across columns and pinpoint your top ten most recent plays.
Excel highlighting a range of cells when I am only trying to highlight a range in a column in a filtered sheet.
Experiencing unexpected range highlighting in filtered Excel sheets, particularly with large datasets, can be frustrating. It appears Excel is expanding your selection beyond the intended column, likely due to the filtering impacting how ranges are recognized. While a specific setting isn't immediately apparent, the filtering is a primary suspect. Explore our article, "Excel + Power Query and Power Automate," for insights into data management techniques that might offer a workaround. We're here to help you transform this challenge into a more streamlined data experience.
Sort Order and Locking Cells
Navigating shared spreadsheets with multiple users can introduce unexpected data shifts. Many users experience this when sorting – a common challenge is maintaining the original order of data after filtering and sorting. To address this, consider locking Column A to preserve the block order while still enabling sorting by Column H (IDs). This ensures a consistent, organized view even after filters are removed.
Should AI Developers Make the Switch from Polars to Pandas?
Not all Python data libraries offer equal performance for AI development. Polars and Pandas are both popular choices, but their architectures differ significantly. This post explores whether AI developers should consider transitioning from Pandas to Polars, particularly given Polars’ optimized query engine and memory efficiency. Discover how these factors impact speed and scalability in modern data workflows. For deeper insights into agentic AI applications, see our recent article, "We built the Agentic World Cup - LLMs that compete in 1v1 Soccer [P]."
The Problem with pandas Isn’t Performance. It’s Cognitive Overhead.
The persistent narrative around pandas focuses on performance bottlenecks, but a more fundamental challenge exists: cognitive overhead. While faster dataframe engines offer incremental gains, they fail to address the core issue—the sheer volume of syntax analysts must manage. This limits productivity and increases the potential for errors. Explore how reducing this mental load, rather than solely chasing speed, unlocks true data fluency. For deeper insights into AI-powered assistance, consider "Instacart Builds Blueberry," which showcases a practical application of this principle.