21 articles tagged #Data Cleaning
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.
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.
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.
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.
When you run str.split() on a Pandas column, rows that don't contain the delimiter come back as NaN and quietly disappear from your results. Here's exactly why it happens and how to fix it without losing a single row.