184 articles tagged #Debugging
If SUMPRODUCT suddenly throws #VALUE! the moment you reference a second sheet, you're not alone. This guide explains exactly why multi-sheet ranges break SUMPRODUCT and gives you working alternatives that actually produce the right totals.
SUMPRODUCT is supposed to multiply and sum your arrays in one shot, but the moment a text value sneaks in, the total goes wrong with no error to warn you. Here's exactly why it happens and how to fix it.
ChatGPT can produce regex patterns that look convincing but silently miss edge cases, match too broadly, or break on Unicode. Here's how to prompt your way to patterns you can actually trust in production.
Your WebSocket works fine in local dev but drops every 60 seconds in production. The culprit is almost always Nginx's default proxy timeout — and the fix is three lines of config once you know where to look.
Your GoRouter redirect logic works perfectly in the emulator but silently does nothing when a real deep link arrives. Here's why it happens and exactly how to fix it without rewriting your routing layer.
AVERAGEIFS returning zero instead of an error or blank is one of those silent failures that corrupt your reports without warning. Here's exactly why it happens and how to fix it cleanly.
Setting the values param in pd.crosstab() gives you aggregated cells, but the All margin row and column often show unexpected totals. Here's exactly why it happens and how to fix it for good.
When you call pandas melt() and your id_vars columns already contain duplicate combinations, the result explodes with far more rows than you expected. Here's exactly why that happens and how to fix it cleanly.
Pandas rolling() silently fills window edges with NaN when there aren't enough rows to satisfy the minimum period. Here's why it happens, how to diagnose it, and exactly how to fix it without losing your analysis.
Pandas sort_values puts NaN at the end by default — but when you're sorting ascending, that silent behavior can corrupt your analysis. Here's exactly how to control where NaN lands, and why it matters.
Calling astype(int) on a Pandas column with NaN values doesn't raise an error — it silently converts those NaNs to zero, corrupting your data without warning. Here's how to detect and fix it properly.
Pandas fillna can silently skip columns with Categorical dtype, leaving NaN values in your DataFrame without any warning. Here's why it happens and exactly how to fix it for good.