--- updatedAt: 2026-10-08T14:40:04.000Z agentTools: projectIndex: https://documentation.picsellia.com/llms.txt --- # Charts `Charts` let you build custom aggregate visualizations over your `Datalake` `Data`, alongside the *Query Language* and *Embeddings* exploration modes detailed [here](https://documentation.picsellia.com/docs/datalake-overview#3-exploration-modes-and-filtering). For instance, you can track the volume of `Data` uploaded over time, or build a histogram of file sizes. For a Computer Vision project, this kind of aggregate view is what surfaces problems that are invisible image by image: a `DataTag` breakdown can reveal a class imbalance before it skews training, a chart grouped by `Source` can confirm all your acquisition sites or cameras are still feeding the `Datalake`, and a volume-over-time chart can flag a sudden drop or spike in ingestion worth investigating. This feature has also been designed to support very large volumes of `Data`. The computation of a chart is highly optimized and performed on Picsellia's side; this way, you do not need to fetch all your `Datalake` and associated properties/`Metadata`/`Tag` locally with the SDK anymore, which is painful and time-consuming, to build custom metrics. Everything is centralized on Picsellia and available for every member of the *Organization*. ## 1. Accessing Charts `Charts` are available in two places: * The *Charts* tab at the top of the `Datalake`, listing every chart created so far.

* The *Charts* option of the exploration mode selector next to the search bar, which displays the `Data` grid alongside the `Charts` panel for side-by-side exploration.

Clicking on a bar of a chart automatically filters the `Data` displayed in the chosen view (*Grid*, *Table*, or *Details*) to only the ones matching that bar, by applying the equivalent query in the search bar. This drill-down is especially useful when `Charts` are opened in the side-by-side exploration mode: spotting an anomaly on a chart (a spike, a drop, an overrepresented `Tag`) and immediately visualizing the corresponding images or videos, right next to it, without having to rebuild the query yourself.

### Charts are available across the platform This same `Charts` mechanism, including the click-to-filter drill-down, is also available on `DatasetVersion` (detailed [here](https://documentation.picsellia.com/docs/dataset-analytics-and-charts)), `Evaluation` (detailed [here](https://documentation.picsellia.com/docs/experiment-evaluation-charts)), and `Deployment` predictions overviews. ### Zoom in your chart You can zoom in on a specific part of your chart by drawing a square around the region of interest, this part will then be focused on. To reset the zoom you simply need to double click anywhere on the Chart.

## 2. Creating a Chart Click *+ Create chart* to open the creation form: * **Name**: the name of your chart. * **Aggregate Function**: `AVG`, `COUNT`, `MAX`, `MIN`, or `SUM`. * **Aggregate Field**: the field to aggregate, for instance `ID` (to count `Data`) or a `Custom Metadata` field. * **Group By Field**: the field used to bucket the results, for instance `CREATED_AT`, `CONTENT_TYPE`, `UPLOAD_STATUS`, `FILE_SIZE`, `TAG`, `SOURCE`, `METADATA`, `CUSTOM_METADATA`, `DATASET_VERSION`, or `DEPLOYMENT`. This field is optional: leaving it empty produces a *Metric* chart, a single aggregate value computed over the whole selection instead of a breakdown. * **JSON Key**: when the aggregated or grouped field is a `Custom Metadata` one, an additional *JSON Key* input tells Picsellia which key to read. It suggests the keys declared in the `Custom Metadata` schema of the `Datalake` (see [Metadata](https://documentation.picsellia.com/docs/datalake-metadata#3-the-custom-metadata-schema)), so you do not have to remember how each one is spelled, and still accepts a key typed by hand when the schema does not declare it. * **Advanced filtering**: optionally scope the chart to a subset of `Data` using the same *Query Language* as the *Search Bar*, detailed [here](https://documentation.picsellia.com/docs/datalake-query-language). It allows the Chart to only consider a subset of Data.

Once created and computed, your chart is displayed as a card showing the resulting visualization, for instance, a bar chart of the *Data count* aggregated by creation date. Please note that the "Aggregate field" and "Group by field" vary depending on the object for which you are creating a `Chart`; indeed, you do not have access to the same properties/fields whether you are creating a `Chart` in your `Datalake`, `DatasetVersion` (annotation- or attributes-related fields will additionally be available), `Evaluation` (COCO metrics-related fields will be available) or `Deployment` predictions. ## 3. Managing Charts From a chart's card, you can: * **Recompute**: refresh the chart with the latest `Data`. * **Update** or **Delete**: from the *...* menu.