Pandas, SQL, Power BI, Tableau, data visualization
41 articles
Your Power BI running total looks perfect on the first page, then resets to zero on the next date or category. Here's why DAX cumulative measures break and exactly how to fix them.
Reshaping data with Pandas melt() and stack() is a common task in analytics pipelines, but many developers are surprised when the resulting dataset contains duplicate rows, inflated record counts, or incorrect aggregations. Learn why these issues occur and how to avoid them.
If your Pandas pivot_table is producing numbers that don't match your expectations, the aggregation is probably running on the wrong columns. Here's how to catch it, understand why it happens, and fix it for good.
Seeing duplicate rows in your Power BI visuals when the source data looks clean? The culprit is almost always a misconfigured relationship. Here's how to diagnose and fix it without guessing.
GROUP BY quietly treats NULL as its own group, but aggregations like COUNT and SUM still skip NULL values inside columns — leaving your totals wrong with no error message. Here's how to catch it before it costs you.
Your read_csv call is crawling on a file that should load in seconds. Before you rewrite your pipeline, check these common culprits — dtype inference, unnecessary columns, and chunking strategy — in the right order.
Your window function totals look off and you can't figure out why. Nine times out of ten, a missing or incorrect PARTITION BY clause is the culprit. Here's how to diagnose it and fix it fast.
Your Power BI report shows blank cells where you expect zeros, and your totals, charts, and slicers start behaving oddly. Here's exactly why DAX returns BLANK by default and how to force it to show zero.
LOD expressions are supposed to give you precise control over aggregation granularity in Tableau — but when they return wrong numbers, diagnosing the cause is maddening. Here's a practical breakdown of every common failure mode and how to fix each one.
Your merge looked clean, but your row count is wrong and you have no idea why. This guide walks you through exactly how to diagnose silent row loss in pandas merge() — join types, key mismatches, duplicates, and more.
Your groupby().sum() looks right but the numbers are off. Before you blame the data, check these common Pandas aggregation traps — duplicate rows, NaN handling, dtype coercion, and more — that silently corrupt your results.
Learn why NumPy broadcasting mismatches can produce silently incorrect calculations, how broadcasting rules work, and practical techniques for detecting and preventing costly data processing errors.