is_na identifies missing or null entries in a Pandas data structure. It returns 1 for NaN and 0 otherwise, so summing the results counts missing values. section 13.2
Counting missing values
For a Titanic dataset, raw_data.isna().sum() reports 177 missing Age values, 687 missing Cabin values, and 2 missing Embarked values. These counts guide whether to remove a column or fill its gaps. section 13.2
Use the result to choose a cleaning action:
- Fill missing numeric values with a median, as with Age, or categorical gaps with a placeholder such as U. section 13.2
