14 articles tagged #Data Processing
Accidentally overwriting worksheets is one of the most common mistakes when using Python's openpyxl library. Whether you're appending reports, updating existing workbooks, or generating automated spreadsheets, understanding how openpyxl handles workbook saving can prevent costly data loss.
Many Python developers are surprised when simple calculations like 0.1 + 0.2 don't equal 0.3. The issue isn't a Python bug—it's how floating-point arithmetic works. Learn why floating-point precision errors occur, when to use Python's decimal module instead, and how to avoid costly numerical mistake
Python's csv.DictReader may appear to skip rows, produce malformed records, or fail unexpectedly when a CSV file is opened with the wrong text encoding. Learn how character encodings affect CSV parsing, how to diagnose encoding-related issues, and how to build reliable CSV import pipelines.
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.
When pandas apply() swallows row-level exceptions without warning, your pipeline keeps running while corrupt results pile up silently. Here's how to detect, debug, and prevent this behavior for good.
Your read_csv call is crawling on a file that should load in seconds. Before you rewrite your pipeline, check these common culprits — dtype inference, unnecessary columns, and chunking strategy — in the right order.
When pd.concat() leaves you with a broken 0-to-N index or hundreds of duplicate rows you didn't expect, the fix is usually one or two parameters away. Here's exactly what's going wrong and how to correct it.
Many Python developers are surprised when Excel formulas written with openpyxl show outdated results even after saving the workbook. Learn why stale formula values occur, how Excel calculates formulas, and the best ways to ensure accurate spreadsheet results.
Every time you call DataFrame.to_excel() in a loop, only the last sheet survives. The root cause is a one-line misunderstanding of how ExcelWriter works — and the fix takes about two minutes once you know it.
Running a Pandas merge() and ending up with far more rows than you expected? Non-unique keys cause a many-to-many join explosion. Here's how to diagnose the root cause and fix it without losing data.
Pandas resample silently returns NaN when your DatetimeIndex has gaps, duplicates, or a missing timezone — and the fix isn't always obvious. Here's exactly why it happens and how to resolve it.
Your Pandas pivot_table looks right but cells show NaN where you expected zeros. This guide explains exactly why it happens and shows you every reliable fix — from fill_value to aggfunc edge cases.