9 articles tagged #Data Preprocessing
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.
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.
You run value_counts() on a column, get a clean frequency table, and only later realize your NaN values were silently dropped. Here's exactly why it happens and how to fix it without losing your missing data signal.
You called dropna() to keep only fully complete rows, but it's quietly dropping rows that still have useful data. Here's exactly why that happens and the parameters you need to get complete-case filtering right.
When pd.to_datetime() quietly turns valid dates into NaT, your analysis is already broken — and you may not notice until much later. Here's exactly why it happens and how to fix it cleanly.
Pandas groupby silently drops None keys by default, leaving your aggregations short and your results confusing. Here's exactly why it happens and how to fix it in three reliable ways.
Scikit-learn pipelines are excellent for preventing data leakage and simplifying preprocessing, but they can sometimes transform your target variable (y) in unexpected ways. Learn why this happens, how it affects model performance, and how to prevent subtle training and evaluation bugs.