Count

Overview

The Count widget provides a consolidated view of record-level statistics for each column in the dataset. It helps users quickly understand data completeness, uniqueness, and duplication by displaying counts of Filled, Null, Distinct, and Non-Distinct values across columns.

What the Widget Analyzes

Profiling dimension: Record-level value counts
Level of analysis: Column-level
Calculation basis:
- Filled: Number of filled or non-null records in a column
- Null: Number of null or missing records in a column
- Distinct: Number of unique values in a column
- Non-Distinct: Number of repeated (non-unique) values in a column
- Total: Total number of records evaluated per column
- Percentages displayed in the chart are calculated relative to the total record count of each column.

What the Widget Shows

Count Widget

A stacked visual comparison of Null, Filled, Distinct, and Non-Distinct values for each column.
A tabular summary listing exact counts for each metric per column.
The ability to switch focus between columns for detailed analysis. Column-level indicators display distribution details (Count details for current vs. last 5 runs) for individual columns.

How to Read This Widget

Each vertical bar represents a single column in the dataset.
Each bar is segmented into color-coded sections such as Null, Filled, Non-Distinct, and Distinct.
The height of each segment indicates the percentage contribution of that metric for the column.
Numeric labels on the bars represent actual record counts.
The legends at the top of the widget map colors to their respective metrics.

Available Views

The widget supports bar chart/graph view that provides a comparative overview, not a drill-down into individual values. Each column label on the X-axis corresponds directly to a column listed in the table.

On the top-right corner of the visualization pane, use the:

Expand icon to visualize a larger view for easier comparison across columns
Collapse icon to restore the widget to its default size

Note:

Hovering over chart points highlights the selected column and category.
Clicking the legend on the right side of the chart allows one of the following:
- Enables that specific column and highlights its corresponding shape in the spider chart view.
- Disables that specific column and hides its corresponding shape in the spider chart view.
All interactions are read-only and do not alter the dataset.

Supporting Panes

The widget includes a Count tabular summary pane on the right, always visible alongside the visualization pane on the left, providing detailed column-level context. It displays:

Column Name - Name of the dataset column
Filled - Count of non-null values
Null - Count of null values
Total - Total number of records evaluated
Distinct - Count of unique values
Non-Distinct: Count of repeated (non-unique) values

Pane Interactions

Providing a column name in the Search column list box filters columns in the table and quickly locates a specific column by name.
Clicking on the column headers sort columns in ascending or descending order.
Clicking the chart icon for each column opens the data distribution (Count details for current vs. last 5 runs) view for that specific column. This enables a transition from summary level counts to value level distribution analysis.
Scrolling allows access to additional columns when the list exceeds visible space.

How to Interpret the Results

Columns with high null counts may indicate data completeness issues.
A low distinct count combined with high non-distinct values may indicate categorical or repetitive data.
Columns with distinct count close to total count may represent identifiers or unique keys.
Balanced filled and distinct values suggest healthy data variability.

When to Use This Widget

To assess data completeness across columns.
To identify columns with missing or sparse data.
To detect potential primary keys or identifiers.
To prioritize columns for further profiling or data quality rule creation.

What's next? Statistical Count