> For the complete documentation index, see [llms.txt](https://documentation.astera.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://documentation.astera.com/astera-data-stack-v9/dataflows/visualizations/distribution-plots.md).

# Distribution Plots

The *Distribution Plots* object allows users to visualize categorical data variables using mainstream plots such as bar charts, pie charts, histograms, and polygons with an interactive interface and several configuration options. It is a useful object to visualize a general profile of your dataset.

In Astera, users can plot these graphs on data with up to half a million categories and display it easily with the drill-down feature.

In this document, we will learn how the *Distribution Plots* object can be used to plot and visualize your data.

**Bar Chart**

A bar chart is a pictorial representation of grouped data in the form of rectangular bars. The height of the bars depends on the aggregates of numeric fields, grouped together by distinct categories of a categorical variable.

Users can construct a simple bar chart as well as a stacked bar. For the following example, you can download the sample data file from here (hyperlink).

**Using Distribution Plots**

1. To get a *Distribution Plots* object from the Toolbox, go to *Toolbox > Visualization > Distribution Plots* and drag-and-drop the plot object onto the dataflow designer.

![](/files/agUR6naIwzjG6TYr6Meb)

2. Auto-map the source fields by dragging and dropping the root node of the source object onto the *Bar* (input) node of the *Distribution Plots* object.

![](/files/Ek70IgUG7w4wqVDGkgdJ)

3. Right-click on the object’s header and select *Properties* from the context menu.

![](/files/A6FyCeScqDMQx8khBepi)

A configuration window will open as shown below. This is the *Layout Builder*, where users have the option to change the name or *Data Type* of the fields, apply expressions, and provide a *Default Value* in case of null and empty records.

![](/files/hDW2p0FiCwJ1BhxqnZ0f)

Click *Next*. Here, users have the option to select *Plot Type* and define plot properties.

![](/files/Qgik8swbrczZG9lqfL32)

4. *Plot Type* is set as *Bar* by default. The drop-down menu of this options contains several plot types.

![](/files/9CZDG6n0XiiG3HXz7pED)

5. Set *Aggregate by* to *Frequency*. The drop-down menu of this option contains 5 aggregate functions.

![](/files/whWAEfAw55tY7A77S7PB)

6. In the *Plot Fields* group box, users can select a *Data Field* to apply the selected aggregate function. Only with *Frequency* aggregate type, the object automatically selects the first mapped field and disables the option, as shown below.

![](/files/dAFa6aXfqE0HSlX2I5iY)

There are additional plot display properties under the *Bar Properties* group box.

* *Horizontal Bars* – Changes the orientation of default vertical bars to horizontal bars.
* *Show Data Labels* – Displays data labels inside bars when the chart is rendered.

Check these options and click *Next*.

A *Labels* screen will appear. Here, users can fill in the labels for *Title*, *Subtitle*, *X-Axis*, and *Y-Axis*.

![](/files/TjFGHF8GiuFNj3QcqhcO)

Click *Next*. An *Additional Options* screen will open, providing the following controls.

![](/files/qOiN5EUfvwN73XKZ65zJ)

**General Plot Options**

* *Scale Axis* – Scales the x-axis and y-axis as per the starting values of respective tables.
* *Inverted Graph* – Inverts the graph by displacing the axis.
* *Data is Sorted* – Sorts incoming data in case it’s unsorted.
* *Enable Data Zoom* – Provides controls to zoom on data points with respect to both axis

Save the plot with .html extension by selecting the *Save Plot* checkbox.

7. To visualize the plot, right-click on the *Distribution Plots* object’s header, and select *Visualize Data* from the context menu.

![](/files/dbRvBkbnai7IYDeeeOEo)

A *Visualization* window will open, displaying the bar chart. You can click on the bars and drill down to next level of categories.

![](/files/A6FyQQDsVYSbkOkjSBEK)

On the same data, users can also plot *Stacked Bar* available in *Plot Types* drop-down menu.

![](/files/LsbUQyiNye5mlwjMwMos)

**Pie Chart**

A pie chart is a pictorial representation of grouped data in the form of sectors of a circle. The area of the sectors depends on the respective percentage proportion of categories in a data field.

Users can construct a simple pie chart, a doughnut chart, and a nested pie.

For the following example, you can download the sample data file from here (hyperlink). A simple pie chart is configured the same way as a bar chart. Therefore, in this example, we will create a nested pie that has different configuration settings.

**Using Distribution Plots**

8. Follow steps 1-3 of *Bar Chart* example.

![](/files/jMhPrLBLc0DiivruH14r)

9. Set *Plot Type* to *Nested Pie*.

![](/files/dRPFGbsirU7zDbBuIe4F)

10. In the *Plot Fields* group box, users can select an *Inner Field* and an *Outer Field* for respective inner and outer pies.

There are additional plot display properties under the *Pie Properties* group box.

* *Outer Chart Type* – Provides two outer chart display options.
  * *Doughnut*:

![](/files/utVIvaYuMOec0ZgV3Rxm)

![](/files/p2clSl1evKDpZ7z4V0Fj)

* *Nightingale:*

![](/files/JAgc9QfdPvZXzhVy74UX)

![](/files/t1fmMNGSXxEoyv4Q1hqE)

This concludes our discussion on using the *Distribution Plots* object in Astera.


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