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Version: Python

join

join joins data from a pair of tables - a left and right table - based upon a set of match columns. The match columns establish key identifiers in the left table that will be used to find data in the right table. Any data types can be chosen as keys, and keys can be constructed from multiple values.

The output table contains rows that have matching values in both tables. Rows that do not have matching criteria will not be included in the result. If there are multiple matches between a row from the left table and rows from the right table, all matching combinations will be included. If no match columns are specified ([]), every combination of left and right table rows is included.

Syntax​

left.join(
    table: Table,
    on: Union[str, Sequence[str]],
    joins: Union[str, Sequence[str]] = None,
) -> Table

Parameters​

ParameterTypeDescription
tableTable

The table data is added from (the right table).

onUnion[str, Sequence[str]]

Columns from the left and right tables used to join on.

  • ["A = B"] will join when column A from the left table matches column B from the right table.
  • ["X"] will join on column X from both the left and right table. Equivalent to "X = X".
  • ["X, A = B"] will join when column X matches from both the left and right tables, and when column A from the left table matches column B from the right table.
  • If this argument is left empty, a cross-join is performed. The result is a table with every possible combination of rows from the two tables.
joins optionalUnion[str, Sequence[str]]

Columns from the right table to be added to the left table based on key may be specified in this list:

  • [] will add all columns from the right table to the left table (default).
  • ["X"] will add column X from the right table to the left table as column X.
  • ["Y = X"] will add column X from right table to left table and rename it to be Y.

Returns​

A new table containing rows that have matching values in both tables. Rows that do not have matching criteria will not be included in the result. If there are multiple matches between a row from the left table and rows from the right table, all matching combinations will be included. If no match columns are specified, every combination of left and right table rows is included.

Examples​

In the following example, the left and right tables are joined on a matching column named DeptID.

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

left = new_table(
    [
        string_col(
            "LastName",
            ["Rafferty", "Jones", "Steiner", "Robins", "Smith", "Rogers", "DelaCruz"],
        ),
        int_col("DeptID", [31, 33, 33, 34, 34, 36, NULL_INT]),
        string_col(
            "Telephone",
            [
                "(303) 555-0162",
                "(303) 555-0149",
                "(303) 555-0184",
                "(303) 555-0125",
                "",
                "",
                "(303) 555-0160",
            ],
        ),
    ]
)

right = new_table(
    [
        int_col("DeptID", [31, 33, 34, 35]),
        string_col("DeptName", ["Sales", "Engineering", "Clerical", "Marketing"]),
        string_col(
            "DeptTelephone",
            ["(303) 555-0136", "(303) 555-0162", "(303) 555-0175", "(303) 555-0171"],
        ),
    ]
)

result = left.join(table=right, on=["DeptID"])

The left table has seven rows of data and the right table has four rows of data, but the result table has five rows. This is because the last two rows of the left table and the last row of the right table have no matches in the DeptID column, so they are not included in the resulting table.

If the right table has columns that need renaming due to an initial name match, a new column name can be supplied in the third argument of the join. In the following example, Telephone is renamed to DeptTelephone.

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

left = new_table(
    [
        string_col(
            "LastName",
            ["Rafferty", "Jones", "Steiner", "Robins", "Smith", "Rogers", "DelaCruz"],
        ),
        int_col("DeptID", [31, 33, 33, 34, 34, 36, NULL_INT]),
        string_col(
            "Telephone",
            [
                "(303) 555-0162",
                "(303) 555-0149",
                "(303) 555-0184",
                "(303) 555-0125",
                "",
                "",
                "(303) 555-0160",
            ],
        ),
    ]
)

right = new_table(
    [
        int_col("DeptID", [31, 33, 34, 35]),
        string_col("DeptName", ["Sales", "Engineering", "Clerical", "Marketing"]),
        string_col(
            "Telephone",
            ["(303) 555-0136", "(303) 555-0162", "(303) 555-0175", "(303) 555-0171"],
        ),
    ]
)

result = left.join(
    table=right, on=["DeptID"], joins=["DeptName, DeptTelephone = Telephone"]
)

In the following example, the left and right tables have multiple matches. The result is the cross product of possible outcomes.

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

left = new_table(
    [
        string_col(
            "LastName",
            ["Rafferty", "Jones", "Steiner", "Robins", "Smith", "Rogers", "DelaCruz"],
        ),
        int_col("DeptID", [31, 33, 33, 34, 34, 36, NULL_INT]),
        string_col(
            "Telephone",
            [
                "(303) 555-0162",
                "(303) 555-0149",
                "(303) 555-0184",
                "(303) 555-0125",
                "",
                "",
                "(303) 555-0160",
            ],
        ),
    ]
)

right = new_table(
    [
        int_col("DeptID", [31, 31, 33, 34, 35, NULL_INT]),
        string_col(
            "DeptName",
            ["Sales", "Support", "Engineering", "Clerical", "Marketing", "Safety"],
        ),
        string_col(
            "DeptTelephone",
            [
                "(303) 555-0136",
                "(303) 555-0187",
                "(303) 555-0162",
                "(303) 555-0175",
                "(303) 555-0171",
                "(303) 555-0145",
            ],
        ),
    ]
)

result = left.join(table=right, on=["DeptID"])

In some cases, the matching columns have different names in the left and right table. Below, the left table has a column name DeptNumber that we want to match to the colomn DeptID in the right table. To perform this match, the second argument needs the name of each column in the left and right tables.

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

left = new_table(
    [
        string_col(
            "LastName",
            ["Rafferty", "Jones", "Steiner", "Robins", "Smith", "Rogers", "DelaCruz"],
        ),
        int_col("DeptNumber", [31, 33, 33, 34, 34, 36, NULL_INT]),
        string_col(
            "Telephone",
            [
                "(303) 555-0162",
                "(303) 555-0149",
                "(303) 555-0184",
                "(303) 555-0125",
                "",
                "",
                "(303) 555-0160",
            ],
        ),
    ]
)

right = new_table(
    [
        int_col("DeptID", [31, 33, 34, 35]),
        string_col("DeptName", ["Sales", "Engineering", "Clerical", "Marketing"]),
        string_col(
            "DeptTelephone",
            ["(303) 555-0136", "(303) 555-0162", "(303) 555-0175", "(303) 555-0171"],
        ),
    ]
)

result = left.join(table=right, on=["DeptNumber = DeptID"], joins=["DeptName"])

In some cases, the matching columns argument is absent. As a result all possible matches are joined.

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

left = new_table(
    [
        string_col(
            "LastName",
            ["Rafferty", "Jones", "Steiner", "Robins", "Smith", "Rogers", "DelaCruz"],
        ),
        int_col("DeptNumber", [31, 33, 33, 34, 34, 36, NULL_INT]),
        string_col(
            "Telephone",
            [
                "(303) 555-0162",
                "(303) 555-0149",
                "(303) 555-0184",
                "(303) 555-0125",
                "",
                "",
                "(303) 555-0160",
            ],
        ),
    ]
)

right = new_table(
    [
        int_col("DeptID", [31, 33, 34, 35]),
        string_col("DeptName", ["Sales", "Engineering", "Clerical", "Marketing"]),
        string_col(
            "DeptTelephone",
            ["(303) 555-0136", "(303) 555-0162", "(303) 555-0175", "(303) 555-0171"],
        ),
    ]
)

result = result = left.join(table=right, on=[""])