Pandas, SQL, Power BI, Tableau, data visualization
41 articles
Are your Tableau dashboards showing incorrect totals, duplicated rows, or missing values? Learn the differences between data blending and joins, why your metrics may be wrong, and how to choose the right approach for accurate business reporting.
Are your SQL CTE and subquery versions returning different row counts? Learn why this happens, how query logic, joins, filtering, and execution plans affect results, and how to debug row count discrepancies across major SQL databases.
Learn how to use DBeaver alongside ChatGPT to debug SQL queries faster, understand execution plans, optimize performance, and solve common SQL errors without replacing your own database expertise.
Wondering why your Pandas groupby() results don't match your data? Learn why groupby() ignores NaN values by default, how the dropna parameter works, and practical techniques to correctly include missing values in your analysis.
Confused about when to use SQL WHERE versus HAVING? Learn how these clauses differ, why aggregation filter bugs occur, and how to write accurate, performant SQL queries with practical examples.
Filtering or dropping rows in a Pandas DataFrame often leaves gaps in the index. Learn when and how to reset the index correctly, avoid common mistakes, and write cleaner, more reliable data processing code.
Are your Tableau calculated fields returning NULL instead of expected values? Learn how to troubleshoot type mismatches, aggregation errors, NULL handling, and calculation order with practical examples and proven best practices.
Is your Excel INDEX MATCH formula returning the wrong value even though the lookup value exists? Learn why unsorted data, incorrect MATCH arguments, duplicate values, and formatting issues cause incorrect results—and how to fix them with practical examples.
A low RMSE doesn't always mean your regression model performs well. Learn why models often struggle with rare or extreme values, how RMSE can hide critical prediction errors, and which evaluation techniques lead to more reliable regression models.
SQL date filters often appear simple, yet subtle mistakes involving BETWEEN, timestamp truncation, time zones, and data types can return missing or unexpected rows. Learn why SQL date range queries fail and how to write reliable, high-performance date filters across modern relational databases.
Is your XLOOKUP formula returning #N/A even though the value clearly exists? The problem may not be your lookup value or range—it could be the match mode argument. Learn how XLOOKUP match modes work, why they produce unexpected results, and how to fix them with practical examples.
Are your Power BI slicers showing incorrect values, blank visuals, or inconsistent totals? The culprit is often an incorrect cross-filter direction between tables. Learn how filter propagation works, why bidirectional relationships cause unexpected behavior, and how to design a clean Power BI data