Why pandas left join returns unexpected rows (and how to fix it) Unexpected rows in pandas left join usually appear in real-world datasets coming from SQL exports, logs, or APIs, where the right DataFrame contains duplicate keys. This leads pandas to generate more rows than expected, often silently breaking downstream logic. ...
Why pandas merge using index returns wrong result (and how to fix it) Incorrect results in pandas merge using index usually appear in real-world datasets from SQL exports, logs, or APIs, where the index contains duplicate values. This leads pandas to generate more rows than expected, often silently breaking downstream logic. ...
Why pandas inner join drops rows unexpectedly (and how to fix it) Row loss in pandas inner join typically surfaces when merging production data where join keys don’t exist in both DataFrames, causing silent data loss that breaks downstream aggregations. ...
Why pandas merge duplicates rows after groupby (and how to fix it) Duplicate rows in pandas merge after groupby usually appear in real-world datasets from SQL exports or logs, where the DataFrame contains duplicate keys after grouping. This leads pandas to generate more rows than expected, often silently breaking downstream logic. ...