Pandas, SQL, Power BI, Tableau, data visualization
35 articles
Most data analysts use Jupyter Notebooks for one-off projects, but the same notebooks can become the foundation of a profitable recurring reporting business. Learn how to automate notebook execution, build client-ready reports, and transform your analytics expertise into a scalable subscription
Many data analysts build powerful Jupyter Notebook reports for internal projects, but few realize these notebooks can become profitable reporting services. Learn how to transform one-off analyses into scalable products, automate report generation, and build a recurring revenue business around data
A Tableau Top N filter often returns unexpected results because filters are evaluated in a specific order. If Context Filters are missing or configured incorrectly, Top N calculations may be performed against the entire dataset instead of the filtered subset. Learn how Tableau's order of operations
Your SQL query returns different row counts depending on which table you JOIN first — and no error is thrown. This guide breaks down exactly why JOIN order changes your results and how to fix it for good.
You ran the same query twice — once with DISTINCT and once with GROUP BY — and got different row counts. This guide breaks down exactly why that happens and how to get the result you actually want.
Power BI's built-in time intelligence functions assume a standard Gregorian calendar. When your data uses fiscal years, 4-4-5 periods, or custom date tables, those functions silently return wrong numbers. Here's how to diagnose and fix it.
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.