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.. _advanced_filters:
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Advanced filters
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================
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``dataset`` provides two methods for running queries: :py:meth:`table.find() <dataset.Table.find>`
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and :py:meth:`db.query() <dataset.Database.query>`. The table find helper method provides
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limited, but simple filtering options::
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results = table.find((column, operator, value))
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# e.g.:
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results = table.find(('name', 'like', '%mole rat%'))
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A special form is using keyword searches on specific columns::
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results = table.find(value=5)
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# equal to:
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results = table.find((value, '=', 5))
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The following comparison operators are supported:
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============== ============================================================
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Operator Description
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============== ============================================================
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gt, > Greater than
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lt, < Less than
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gte, >= Greater or equal
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lte, <= Less or equal
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!=, <>, not Not equal to a single value
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in Value is in the given sequence
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like, ilike Text search, ILIKE is case-insensitive. Use ``%`` as a wildcard
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between, .. Value is between two values in the given tuple
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startswith String starts with
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endswith String ends with
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============== ============================================================
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Querying for a specific value on a column that does not exist on the table
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will return no results.
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Queries using raw SQL
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---------------------
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To run more complex queries with JOINs, or to perform GROUP BY-style
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aggregation, you can also use :py:meth:`db.query() <dataset.Database.query>`
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to run raw SQL queries instead. This also supports paramterisation to avoid
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SQL injections.
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Finally, you should consider falling back to SQLAlchemy_ core to construct
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queries if you are looking for a programmatic, composable method of generating
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SQL in Python.
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.. _SQLALchemy: https://docs.sqlalchemy.org/ |