Dealing with huge datasets, using the Elasticsearch Sample and Diversified Sampler Aggregation as a prefilter

Hi!

it would be cool to be able to use the `sampler` aggregation like this:

```python
df['agent.name'].es_sampler(1000).value_counts()
```

In environments, e.g. the one I am currently working on, we receive up to 2.5 TB of data every 30 minutes. So querying the entire documents for 100% accuracy is not worth the cpu%. The sampler would allow us to look at a subset of that adequately and comprehensively.

Start by tracing the DataFrame expression `df['agent.name']` and its `value_counts()` path, then inspect how Elasticsearch aggregations are represented. Compare that path with Elasticsearch's Sample and Diversified Sampler Aggregations; done means a sampler-based prefilter can be requested for this workflow with the proposed `es_sampler(1000)` usage.

Written by the indexing model from the issue text.