Detecting Missing Data

Real data has gaps. Before analysis, you need to find them.

Check for missing values:

df.isnull()

This returns a DataFrame of True/False for each cell.

Count missing values per column:

df.isnull().sum()

Percentage missing:

df.isnull().sum() / len(df) * 100

Find rows with any missing value:

df[df.isnull().any(axis=1)]

Missing data affects calculations and can indicate data quality issues. Always check before proceeding.

I cover missing data strategies in The Ultimate Pandas Bootcamp.