RowMapping

class lsst.daf.relation.iteration.RowMapping(unique_key: Sequence[ColumnTag], rows: Mapping[tuple, Mapping[ColumnTag, Any]])

Bases: MaterializedRowIterable

A RowIterable backed by a Mapping

Parameters:

unique_keySequence [ ColumnTag ]

Sequence of columns to extract into a tuple to use as keys in the mapping, guaranteeing uniqueness over these columns.

rowscollections.abc.Mapping

Nested mapping with tuple keys and row values, where each row is (as usual for RowIterable types) itself a Mapping with ColumnTag keys.

Methods Summary

materialized()	Convert this iterable to one that holds its rows in a Python collection of some kind, instead of generating them lazily.
sliced(start, stop)	Apply a slice operation to this RowIterable.
to_mapping(unique_key)	Convert this iterable to a RowMapping, unless it already is one.
to_sequence()	Convert this iterable to a RowSequence, unless it already is one.

Methods Documentation

materialized() → MaterializedRowIterable

Convert this iterable to one that holds its rows in a Python collection of some kind, instead of generating them lazily.

Returns:

rowsMaterializedRowIterable

A RowIterable that isn’t lazy.

sliced(start: int, stop: int | None) → RowIterable

Apply a slice operation to this RowIterable.

Parameters:

startint

Start index.

stopint or None

Stop index (one-past-the-end), or None to include up through the last row.

Returns:

rowsRowIterable

Iterable representing the slice. May or may not be lazy.

to_mapping(unique_key: Sequence[ColumnTag]) → RowMapping

Convert this iterable to a RowMapping, unless it already is one.

Parameters:

unique_keySequence [ ColumnTag ]

Sequence of columns to extract into a tuple to use as keys in the mapping, guaranteeing uniqueness over these columns.

Returns:

rowsRowMapping

A RowIterable backed by a mapping.

to_sequence() → RowSequence

Convert this iterable to a RowSequence, unless it already is one.

Returns:

rowsRowSequence

A RowIterable backed by a sequence.