84 articles tagged #Data Analysis
Is your Excel SUMIFS formula returning zero even though matching data exists? Learn why cross-sheet criteria references fail, how to diagnose the issue, and the best ways to fix range mismatches, formatting problems, hidden spaces, and data type inconsistencies.
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.
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.
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.
Does pandas.read_excel() seem to skip rows or load incorrect column names? In many Excel files, the actual header isn't on the first row because of titles, merged cells, or metadata. Learn why this happens, how the header parameter works, and the best ways to import Excel data accurately.
Your Pandas time series suddenly fills with NaN values after calling resample() or asfreq(). In most cases, Pandas isn't losing data—it is exposing missing timestamps in your dataset. Learn why these gaps appear and how to handle them correctly for reliable time series analysis.
Your pandas.merge() operation suddenly doubles or triples the number of rows, even though the merge completes successfully. The cause is usually non-unique join keys that produce many-to-many relationships. Learn why duplicate rows appear after merges and how to validate, diagnose, and fix them
DISTINCT and GROUP BY often appear to produce identical results, leading many developers to use them interchangeably. However, they solve different problems, and misunderstanding their behavior can produce incorrect reports, hidden duplicates, misleading aggregations, and poor query performance.
Your SUMIFS formula looks right, but the total is suspiciously low. The culprit is almost always invisible trailing spaces in your criteria column — here's how to find them and fix them fast.
VLOOKUP can only search the leftmost column of your table array — anything else gives you #N/A. Here's why it happens and three practical ways to fix it without rearranging your entire spreadsheet.
Your Pandas pivot_table looks right but returns NaN in half the cells. The culprit is almost always an aggfunc receiving an empty group — here's how to diagnose and fix it cleanly.
Your SUMIFS formula looks correct, but it returns zero — and the culprit is a #N/A, #VALUE!, or #REF! lurking in your criteria range. Here's exactly why it breaks and three reliable ways to fix it.