NullColumn / New Layer
Create a new column filled with null values of specified type. Similar to pandas.Series(dtype=type) filled with None/null values. Useful for creating placeholder columns, representing missing data, or preparing data structures for future updates.
ColType
enumThe data type of the null column. Maps to Python/numpy data types (e.g., Int64 is similar to np.int64, Float64 to np.float64).
64-bit integer null column. Range when filled: -2^63 to 2^63-1. Suitable for large numbers or timestamps.
32-bit integer null column. Range when filled: -2^31 to 2^31-1. Common for general integer data.
16-bit integer null column. Range when filled: -32,768 to 32,767. Good for small range integers.
8-bit integer null column. Range when filled: -128 to 127. Ideal for small numbers or flags.
64-bit unsigned integer null column. Range when filled: 0 to 2^64-1. For large positive numbers.
32-bit unsigned integer null column. Range when filled: 0 to 4,294,967,295. For moderate positive numbers.
16-bit unsigned integer null column. Range when filled: 0 to 65,535. For small positive numbers.
8-bit unsigned integer null column. Range when filled: 0 to 255. For byte values or small counts.
64-bit double-precision floating point null column. Approximately 16 decimal digits precision. For high-precision numeric data.
32-bit single-precision floating point null column. Approximately 7 decimal digits precision. For general numeric data.
Boolean null column. Can hold null and, when filled: true/false values. For logical flags or binary states.
UTF-8 encoded string null column. Supports international characters and symbols when filled. For text data of any length.
AsColumn
nameName 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.