max_by returns the maximum value for each group. Null values are ignored.
| Parameter | Type | Description |
|---|---|---|
| by optional | Union[str, list[str]] | The column(s) by which to group data.
|
A new table containing the maximum value for each group.
In this example, max_by returns the maximum value for each column.
In this example, max_by returns the maximum value, as grouped by X.
In this example, max_by returns the maximum value, as grouped by X and Y.
table.max_by(by: Union[str, list[str]]) -> Table
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.max_by()
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.max_by(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.max_by(by=["X", "Y"])