> ## Documentation Index > Fetch the complete documentation index at: https://docs.pixeltable.com/llms.txt > Use this file to discover all available pages before exploring further. # ResultSet > View Source on GitHub # class  pixeltable.ResultSet A dataset obtained by executing a [`Query`](./query). Returned by [`Query.collect()`](./query#method-collect), [`Query.head()`](./query#method-head), [`Query.tail()`](./query#method-tail), and the equivalent methods on class [`Table`](./table). A `ResultSet` is structured as a table with rows (indexed by integers) and columns (indexed by strings). The column names correspond to the expressions in the query's select list. The values in a `ResultSet` can be accessed in various ways: * `len(result)` returns the number of rows * `result[i]` returns the `i`th row as a `dict` mapping column names to values * `result['col']` returns a `list` of all values in the column named `'col'` * `result[i, 'col']` returns the specific value in the `i`th row and column `'col'` `ResultSet` implements the Sequence protocol, so it can be iterated over and converted to other sequence types in the usual fashion; for example: * `for row in result` (iterates over rows) * `list(result)` (converts to a list of rows) ## method  to\_pandas() ```python Signature theme={"theme":{"light":"light-plus","dark":"dark-plus"}} to_pandas() -> pd.DataFrame ``` Convert the `ResultSet` to a Pandas `DataFrame`. **Returns:** * `pd.DataFrame`: A `DataFrame` with one column per column in the `ResultSet`. ## method  to\_pydantic() ```python Signature theme={"theme":{"light":"light-plus","dark":"dark-plus"}} to_pydantic(model: type[BaseModelT]) -> Iterator[BaseModelT] ``` Convert the `ResultSet` to Pydantic model instances. **Parameters:** * **`model`** (`type[BaseModelT]`): A Pydantic model class. **Returns:** * `Iterator[BaseModelT]`: An iterator over Pydantic model instances, one for each row in the result set.