Method where

tables/table.py:1479–1575

r"""Iterate over values fulfilling a condition. This method returns a Row iterator (see :ref:`RowClassDescr`) which only selects rows in the table that satisfy the given condition (an expression-like string). The condvars mapping may be used to define the variable n

(
        self,
        condition: str,
        condvars: dict[str, Column | np.ndarray] | None = None,
        start: str | None = None,
        stop: str | None = None,
        step: str | None = None,
    )

        return frozenset(idxcols)

    def where(
        self,
        condition: str,
        condvars: dict[str, Column | np.ndarray] | None = None,
        start: str | None = None,
        stop: str | None = None,
        step: str | None = None,
    ) -> Iterator[tableextension.Row]:
        r"""Iterate over values fulfilling a condition.

        This method returns a Row iterator (see :ref:`RowClassDescr`) which
        only selects rows in the table that satisfy the given condition (an
        expression-like string).

        The condvars mapping may be used to define the variable names appearing
        in the condition. condvars should consist of identifier-like strings
        pointing to Column (see :ref:`ColumnClassDescr`) instances *of this
        table*, or to other values (which will be converted to arrays). A
        default set of condition variables is provided where each top-level,
        non-nested column with an identifier-like name appears. Variables in
        condvars override the default ones.

        When condvars is not provided or None, the current local and global
        namespace is sought instead of condvars. The previous mechanism is
        mostly intended for interactive usage. To disable it, just specify a
        (maybe empty) mapping as condvars.

        If a range is supplied (by setting some of the start, stop or step
        parameters), only the rows in that range and fulfilling the condition
        are used. The meaning of the start, stop and step parameters is the
        same as for Python slices.

        When possible, indexed columns participating in the condition will be
        used to speed up the search. It is recommended that you place the
        indexed columns as left and out in the condition as possible. Anyway,
        this method has always better performance than regular Python
        selections on the table.

        You can mix this method with regular Python selections in order to
        support even more complex queries. It is strongly recommended that you
        pass the most restrictive condition as the parameter to this method if
        you want to achieve maximum performance.

        .. warning::

            When in the middle of a table row iterator, you should not
            use methods that can change the number of rows in the table
            (like :meth:`Table.append` or :meth:`Table.remove_rows`) or
            unexpected errors will happen.

        Examples
        --------
        ::

            passvalues = [ row['col3'] for row in
                           table.where('(col1 > 0) & (col2 <= 20)', step=5)
                           if your_function(row['col2']) ]
            print("Values that pass the cuts:", passvalues)