ColumnsByType / Select Layer

Select columns by specific data types with precise control over the bit width and representation. Similar to pandas select_dtypes() but with fine-grained type control. Useful for:

Memory optimization
Type-specific operations
Data validation
System integration
Storage format compliance

Table

DataTypes

[, ...]

Container for data type selection criteria. Allows multiple type selections for flexible column filtering.

DataType

enum

Boolean

Available data types for precise column selection. Provides granular control over numeric precision and storage requirements.

Boolean ~

True/False columns. Used for flags, indicators, and binary states

UInt8 ~

8-bit unsigned integers (0 to 255). Ideal for small positive numbers and byte values

UInt16 ~

16-bit unsigned integers (0 to 65,535). Good for moderate-range positive counts

UInt32 ~

32-bit unsigned integers (0 to 4,294,967,295). Suitable for large positive numbers

UInt64 ~

64-bit unsigned integers (0 to 18,446,744,073,709,551,615). For very large counts

Int8 ~

8-bit signed integers (-128 to 127). For small numbers with sign

Int16 ~

16-bit signed integers (-32,768 to 32,767). For moderate-range integers

Int32 ~

32-bit signed integers (-2^31 to 2^31-1). Common for general integer data

Int64 ~

64-bit signed integers (-2^63 to 2^63-1). For large numbers requiring sign

Float32 ~

32-bit floating point. For decimal numbers with ~7 digits precision

Float64 ~

64-bit floating point. For high-precision decimal numbers (~15 digits)

Date ~

Calendar dates without time component. For birth dates, deadlines, events

Time ~

Time of day without date component. For schedules, daily events

String ~

Text data. For names, descriptions, identifiers, and general text