81 articles tagged #Data Analysis
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.
Running a groupby aggregation and noticing some columns vanish from the result? Pandas silently drops columns it can't aggregate due to mixed types. Here's exactly why it happens and how to fix it for good.
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.
Pandas read_csv quietly converts large integers to float64 when values exceed int64 range, mangling your data without a single warning. Here's exactly why it happens and how to fix it for good.