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Version: Python

select

The select method creates a new in-memory table that includes one column for each argument. Any columns not specified in the arguments will not appear in the resulting table.

When using select, the entire requested dataset is evaluated and stored in memory.

note

The syntax for the select and view methods is identical, as is the resulting table. select is recommended when:

  1. not 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 view, update_view, update, or lazy_update. These methods have different memory and computation expenses.

Syntax​

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

Parameters​

ParameterTypeDescription
formulasUnion[str, Sequence[str]]

Formulas to compute columns in the new table:

  • NULL: returns all columns
  • Column from table: "A" (equivalent to "A = A")
  • Renamed column from table: "X = A"
  • Calculated column: "X = A * sqrt(B)"

Returns​

A new in-memory table that includes one column for each argument. If no arguments are provided, there will be one column for each column of the source table.

Examples​

In the following example, select has zero arguments. All columns are selected. While this selection appears to do nothing, it is creating a compact, in-memory representation of the input table, with all formulas evaluated.

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.select()

In the following example, column B is selected for the new table.

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.select(formulas=["B"])

In the following example, the new table contains source column A (renamed as X), and column B.

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.select(formulas=["X = A", "B"])

In the following example, mathematical operations are evaluated and stored in memory for the new table.

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.select(formulas=["A", "X = B", "Y = sqrt(C)"])