Detecting Missing Values

Missing data is common in real-world datasets. Pandas represents missing values as NaN (Not a Number) or None.

Identifying Missing Values

Use isnull() or isna() to detect missing values:

Counting Missing Values

Get counts of missing data:

Percentage of Missing

Calculate what fraction is missing:

Finding Non-Missing Values

Use notna() to find valid data:

Checking for Any/All Missing

Quick checks across data:

Summary of Missing Data

Create a comprehensive missing data report:

Exercise: Count Missing

Exercise: Filter Valid Rows

Key Points

• NaN and None represent missing values
• isnull() / isna() detect missing (returns True)
• notna() detects present values (returns True)
• .sum() counts missing per column/row
• .any() / .all() for quick checks
• Calculate percentages with / len(df) * 100