Table Features
| Feature | Description | Actions |
|---|---|---|
| Sorting | Reorder data by any column. Useful for quickly identifying extremes such as highest volume, largest transfers, or top-performing tokens. | • Click a column header to sort • Toggle between ascending and descending order • Reset to the default view |
| Filtering | Narrow down large datasets. Essential for isolating relevant records in large datasets. | • Filter by value or keyword • Apply multiple filters simultaneously • Clear filters to reset the view |
| Metric Selection | Adjust which data points are displayed. Helps tailor tables to your specific analysis goals. | • Switch between available metrics • Compare multiple values side by side • Show or hide columns based on relevance |
| Viewing Details | Access more context directly from the table. Allows quick inspection without leaving the table view. | • Click rows to expand details • Hover over cells for tooltips • View linked or related data |
| Pagination | Navigate large result sets efficiently. Helps maintain performance while working with large datasets. | • Move between pages • Jump to specific sections • Adjust number of rows displayed |
| Exporting Data | Move table data outside Zark when needed. Ideal for reporting, modeling, or deeper offline analysis. | • Export as CSV • Copy selected rows or cells • Share datasets with external tools |
Common Table Types
| Table Type | Use Case | Typical Columns |
|---|---|---|
| Price Tables | Market overview and comparison | Token price, Percentage change, Market capitalization, Trading volume |
| Trading Volume Tables | Analyze market activity | Volume by token or pair, Volume change over time, Trade counts, Average trade size |
| Transaction Tables | On-chain analysis | Transaction hashes, Sender and receiver addresses, Timestamps, Token amounts and status |
Best Practices
| Practice Area | Guidelines |
|---|---|
| Working with Tables | • Sort by the metric that matches your goal • Toggle metrics instead of exporting prematurely • Filter aggressively to reduce noise |
| Analyzing Data | • Compare multiple columns together • Look for sudden spikes or drops • Identify outliers and anomalies • Use exports for deeper analysis when needed |