> ## Documentation Index > Fetch the complete documentation index at: https://docs.databar.ai/llms.txt > Use this file to discover all available pages before exploring further. # Columns > Column types, management, and grouping in Databar tables. Columns define the structure of your table. Each column has a name and a type that determines how its values are stored and displayed. You can add columns manually, or they are created automatically when you attach an enrichment. ## Adding columns Click the **+** button at the end of the column header row to add a new column. Give it a name and select a type. Columns created by enrichments are added automatically and named based on the data field they return. ## Managing columns Click any column header to open the column menu. From here you can: * **Rename** the column * **Delete** the column * **Change the column type** * **Sort** the table by this column (ascending or descending) * **Filter** by this column's values * **Hide** the column (use the toolbar to show hidden columns again) * **Pin** the column to the left or right side of the table so it stays visible while scrolling horizontally * **Remove duplicates** based on this column's values (see [Deduplication](/product-guide/deduplication)) * **Download images** as a zip file (Image columns only) You can also **resize** columns by dragging the edge of any column header, and **reorder** columns by dragging the column header to a new position. ## Column types Each column has a type that controls how values are stored, validated, and displayed. Free-form strings. This is the default type for new columns and most enrichment results. Text columns can hold any value including names, emails, descriptions, and identifiers. Integers and decimal values. Numbers are right-aligned in cells and can be sorted numerically. Use this for quantities, scores, employee counts, and similar numeric data. Same as Number but formatted with a currency symbol. Use this for revenue, pricing, funding amounts, and other monetary values. The display includes appropriate formatting while the underlying value remains a number. True/false values displayed as checkboxes. Click the checkbox to toggle the value. Useful for tracking completion, verification status, or any binary state. Date values with a configurable display format. Databar automatically converts common date formats, including UNIX timestamps, into human-readable dates. A date paired with a time value. Use this when you need to track both the day and the specific time, such as event timestamps, log entries, or scheduled actions. Clickable links. Values are rendered as hyperlinks that open in a new tab when clicked. Useful for website URLs, LinkedIn profiles, social links, and any web addresses. Inline image previews rendered from URLs. When a cell contains an image URL, the table displays a thumbnail preview directly in the cell. Click to view the full image. Nested objects and arrays displayed with an expandable viewer. Many API providers return structured JSON data. Click a JSON cell to open the expanded viewer for easier navigation of nested structures. You can use the **JSON Expander** to flatten specific fields from a JSON column into their own top-level columns. See [JSON Expander](/product-guide/json-expander) for details. A single-value dropdown. Define a set of options and pick one per cell. Useful for categorizing rows with statuses, labels, or stages. A multi-value dropdown. Select one or more options per cell. Useful for tagging rows with multiple labels, categories, or attributes. ### Changing a column's type You can change an existing column's type by clicking the column header and selecting a new type. Databar will attempt to coerce existing values to the new type. For example, converting a Text column to Number will parse numeric strings into numbers. If your column contains values that cannot be converted to the new type, those values may be lost. For example, changing a Text column with non-numeric values to Number will clear any cells that don't contain valid numbers. ## Column grouping When an enrichment returns multiple fields, those columns are grouped together under a collapsible header. This keeps your table readable even when dozens of data points are attached to each row.

Grouped columns can be collapsed or expanded by clicking the group header. This is especially useful when working with enrichments that return many fields (e.g., company data with name, description, employee count, funding, and more). ### User-created vs. enrichment columns * **User-created columns** are the columns you add manually. They appear as standalone columns in your table. * **Enrichment columns** are created automatically when you attach an enrichment. They are grouped under the enrichment name and display cell-level [enrichment statuses](/product-guide/tables-overview#cell-level-enrichment-statuses) (success, no data, error, pending). Enrichment columns have additional options in their column menu, including a **Settings** shortcut to open the enrichment sidebar and an **Actions** submenu for running the enrichment on all rows, empty rows only, rows with errors, or filtered rows. ## Related Learn how tables work in Databar Remove duplicate rows based on column values Flatten JSON fields into their own columns Attach data providers to your columns