## Is your feature request related to a problem?

There's only 2 files where we use pandas, but pandas is 49MB and numpy is another 86MB, slowing down builds massively.

## Describe the solution you'd like

Replace pandas with a pure SQL/Python solution in
- posthog/queries/trends.py
- posthog/queries/sessions.py

Both of those have equivalent examples in Clickhouse (ee/clickhouse) that don't use Pandas.

## Describe alternatives you've considered

Continue using Pandas and clogging builds

## Additional context



#### *Thank you* for your feature request – we love each and every one!

posthog/queries/trends.py
events = process_entity_for_events(entity=entity, team_id=team_id, order_by="-timestamp",)
        items, filtered_events = aggregate_by_interval(
            events=events,
            team_id=team_id,
            entity=entity,
            filter=filter,
            breakdown="properties__{}".format(filter.breakdown) if filter.breakdown else None,
        )
        formatted_entities: List[Dict[str, Any]] = []
        for value, item in items.items():
            new_dict = append_data(dates_filled=list(item.items()), interval=filter.interval)
            if value != "Total":
                new_dict.update(breakdown_label(entity, value))
            new_dict.update(
                {
                    "aggregated_value": get_aggregate_breakdown_total(
                        filtered_events, filter, entity, team_id, new_dict["breakdown_value"]
                    )
                }
            )
            formatted_entities.append(new_dict)
        return formatted_entities
    def _format_serialized(self, entity: Entity, result: List[Dict[str, Any]]):