4 articles tagged #ETL
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
Reshaping data with Pandas melt() and stack() is a common task in analytics pipelines, but many developers are surprised when the resulting dataset contains duplicate rows, inflated record counts, or incorrect aggregations. Learn why these issues occur and how to avoid them.
Merging multiple sheets into one with xlsxwriter and ending up with headers sprinkled throughout your data? Here's exactly why that happens and how to write the merge loop that gets it right every time.