Dropping Missing Values

Sometimes removing rows or columns with missing data is the best approach.

Drop Rows with Any Missing

Remove rows containing any NaN:

Drop Rows with All Missing

Only drop if entire row is NaN:

Drop Columns with Missing

Remove columns instead of rows:

Threshold-Based Dropping

Keep rows/columns with minimum valid values:

Drop Based on Specific Columns

Check only certain columns for missing:

In-Place Dropping

Modify the original DataFrame:

Exercise: Drop Incomplete Rows

Exercise: Subset Columns

Key Points

• dropna() removes rows with any NaN
• how='all' removes only if all values missing
• axis=1 removes columns instead of rows
• thresh=n keeps rows/cols with >= n valid values
• subset= checks only specific columns
• inplace=True modifies original DataFrame