Pandas Missing Values: How dropna, fillna, and isna Affect Your Data

Data analyst Michael Nocito published a practical guide on August 8, 2026, explaining how to handle missing values in pandas using dropna, fillna, and isna methods. The tutorial uses a 16-row orders table with 7 deliberately removed cells to demonstrate real-world behavior of each approach. A key finding highlighted is that the default dropna() removes entire rows if any single column has a missing value, discarding 4 of 16 rows — 25% of the data — to fix just 7 cells. The guide also illustrates how filling missing values with the mean artificially reduces data spread, since all filled points cluster at the average rather than reflecting true variation. Nocito emphasizes that each method carries an implicit assumption about the data, and analysts should run df.isna().sum() and compare row counts before choosing any fix.