DropNulls / Manipulation Layer
Remove rows containing null values (missing data). Similar to pandas' dropna() or R's na.omit().
Key features:
- Works with all data types (numeric, string, boolean, etc.)
- Configurable column selection
- Flexible removal criteria (any/all conditions)
Common applications:
- Data cleaning for analysis
- Ensuring dataset completeness
- Preparing data for algorithms that can't handle nulls
- Quality control in datasets
- Database export preparation
Note: This operation specifically handles null values (missing data), distinct from NaN values in floating-point columns. For handling NaN values, use ManipulationDropNans instead.
Table
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Table
Select
[column, ...]Columns to check for null values. Examples:
- Required form fields
- Key identifiers
- Critical measurements If empty, checks all columns. Note: This operation handles null values (missing data) while preserving NaN values in floating-point columns.
How
enumStrategy for row removal based on null presence across specified columns. Controls the balance between data completeness and data preservation.
Any ~ All ~
Remove rows if any specified column contains null. Stricter cleaning that ensures complete data across specified columns. Use cases:
- Required field validation
- Mandatory data completeness
- Analysis requiring full records
Remove rows only if all specified columns contain null. Preserves rows with partial data. Appropriate for:
- Sparse data handling
- Maximizing data retention
- Partial record analysis