Skip to main content
Version: Python

update

The update method creates a new table containing a new, in-memory column for each argument.

When using update, the new columns are evaluated and stored in memory. Existing columns are referenced without additional memory allocation.

note

The syntax for the update, update_view, and lazy_update methods is identical, as is the resulting table. update is recommended when:

  1. all the source columns are desired in the result,
  2. the formula is expensive to evaluate,
  3. cells are accessed many times, and/or
  4. a large amount of memory is available.

When memory usage or computation needs to be reduced, consider using select, view, update_view, or lazy_update. These methods have different memory and computation expenses.

Syntax​

update(formulas: Union[str, Sequence[str]]) -> Table

Parameters​

ParameterTypeDescription
formulasUnion[str, Sequence[str]]

Formulas to compute columns in the new table; e.g., "X = A * sqrt(B)".

Returns​

A new table that includes all the original columns from the source table and the newly defined in-memory columns.

Examples​

In the following example, the new columns (A, X, and Y) allocate memory and are immediately populated with values. Columns B and C refer to columns in the source table and do not allocate memory.

from deephaven import new_table
from deephaven.column import string_col, int_col

source = new_table(
    [
        string_col("A", ["The", "At", "Is", "On"]),
        int_col("B", [1, 2, 3, 4]),
        int_col("C", [5, 6, 7, 8]),
    ]
)

result = source.update(formulas=["A", "X = B", "Y = sqrt(C)"])