CastDatatype / Manipulation Layer

Convert columns to different data types. Similar to pandas' astype() or R's as.type() functions.

Key features:

Multiple data type options
Batch conversion support
Safe type conversion handling

Common applications:

Memory optimization
Data validation
Format standardization
Database compatibility
Algorithm requirements

Note: Conversions that could lose data precision or cause overflow will raise errors unless explicitly handled.

Table

Transforms

[, ...]

List of column type conversions to perform. Multiple transforms allow batch processing of type conversions across different columns.

Select

column

Source column for type conversion. The current type and data content should be compatible with the target data type to avoid conversion errors.

Datatype

enum

Int8

Available target data types for conversion. Choose based on requirements for precision, range, and memory usage.

Int8 ~

8-bit signed integer (-128 to 127). Use for:

Small categorical codes
Byte-sized data
Memory-efficient counters

Int16 ~

16-bit signed integer (-32,768 to 32,767). Ideal for:

Year values
Medium-range counts
Audio sample data

Int32 ~

32-bit signed integer (-2^31 to 2^31-1). Common for:

General purpose integers
Population counts
Time differences

Int64 ~

64-bit signed integer (-2^63 to 2^63-1). Suitable for:

Large counts
Timestamps
Big integer calculations

Uint8 ~

8-bit unsigned integer (0 to 255). Perfect for:

Color values
Small positive counts
Binary flags

Uint16 ~

16-bit unsigned integer (0 to 65,535). Use for:

Port numbers
Image pixel values
Medium positive counts

Uint32 ~

32-bit unsigned integer (0 to 2^32-1). Good for:

Large positive counts
File sizes
Network addresses

Uint64 ~

64-bit unsigned integer (0 to 2^64-1). Ideal for:

Very large counts
Unique identifiers
Microsecond timestamps

Float32 ~

32-bit floating point (single precision). Used for:

Basic scientific calculations
Memory-efficient decimals
Graphics coordinates

Float64 ~

64-bit floating point (double precision). Standard for:

Financial calculations
Scientific computing
High-precision analytics

String ~

Text data type. Essential for:

Human-readable data
Textual features
Identifier preservation

Categorical ~

Optimized storage for repeated strings. Perfect for:

Factor variables
Enumerated types
Grouped text data

Bool ~

Boolean true/false values. Used for:

Binary flags
Condition indicators
Yes/No data

Name for the new column. If not provided, the system generates a unique name. If AsColumn matches an existing column, the existing column is replaced. The name should follow valid column naming conventions.