group_by
group_by
groups column content into vectors.
Syntax
table.group_by(by: Union[str, list[str]]) -> Table
Parameters
Parameter | Type | Description |
---|---|---|
by optional | Union[str, list[str]] | The column(s) by which to group data.
|
Returns
A new table containing grouping columns and grouped data. Column content is grouped into vectors.
Examples
In this example, group_by
creates a vector of values for each column.
from deephaven import new_table
from deephaven.column import string_col, int_col
source = new_table(
[
string_col("X", ["A", "B", "A", "C", "B", "A", "B", "B", "C"]),
string_col("Y", ["M", "N", "O", "N", "P", "M", "O", "P", "M"]),
int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
]
)
result = source.group_by()
- source
- result
In this example, group_by
creates a vector of values, as grouped by X
.
from deephaven import new_table
from deephaven.column import string_col, int_col
source = new_table(
[
string_col("X", ["A", "B", "A", "C", "B", "A", "B", "B", "C"]),
string_col("Y", ["M", "N", "O", "N", "P", "M", "O", "P", "M"]),
int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
]
)
result = source.group_by(by=["X"])
- source
- result
In this example, group_by
creates a vector of values, as grouped by X
and Y
.
from deephaven import new_table
from deephaven.column import string_col, int_col
source = new_table(
[
string_col("X", ["A", "B", "A", "C", "B", "A", "B", "B", "C"]),
string_col("Y", ["M", "N", "O", "N", "P", "M", "O", "P", "M"]),
int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
]
)
result = source.group_by(by=["X", "Y"])
- source
- result