Step-by-step tutorials on Excel, Python, databases, and developer tools
31 articles
After xlrd 2.0 dropped support for xlsx files, countless Python scripts started throwing errors overnight. Here's exactly what changed, why it happened, and how to fix your code the right way.
Written an Excel file with openpyxl and opened it to find all your formulas showing old numbers or zeros? This is a known quirk with cached formula values, and there are several ways to fix it depending on your use case.
Large file uploads with Paramiko often complete without raising an exception, yet the remote file ends up truncated or corrupt. Here's how to diagnose the problem and make your SFTP transfers reliable.
If your CSV file looks fine in Excel but DictReader silently drops rows or mangles keys, a mismatched encoding is usually the culprit. Here's how to diagnose and fix it without losing data.
Every time you run your Python script, openpyxl wipes out your existing Excel sheets and starts from scratch. Here's exactly why that happens and how to fix it so your data survives every save.
You added a Retry adapter to your requests Session, but failed requests still raise errors immediately. The fix isn't obvious — here's exactly why retries silently do nothing and how to wire them up correctly.
If your DataFrame explodes in size after a merge, your join keys probably aren't unique. Here's how to diagnose the problem, understand why it happens, and fix it before it silently corrupts your analysis.
When pandas read_excel quietly drops your data because the header isn't on row one, it can cost you hours of debugging. Here's how to diagnose the problem and fix it with the right parameters.
XLOOKUP returning #N/A even though the value clearly exists in your table? The culprit is almost always the match_mode argument. Here's exactly how to diagnose and fix it in minutes.
INDEX MATCH is supposed to be foolproof, but if your data isn't sorted the way the formula expects, it silently returns the wrong answer. Here's exactly why it happens and how to fix it in under five minutes.
After filtering rows or dropping duplicates in Pandas, your index turns into a scattered mess of non-consecutive integers. Here's exactly how to fix it and avoid the subtle bugs that broken indexes cause downstream.
Pandas drops NaN keys from groupby results by default — and it does so without a warning. If your aggregations look slightly off and you can't figure out why, missing group keys are likely the culprit. Here's how to catch and fix it.