2D Point Plot

Modified on Thu, 09 Feb 2023 at 05:08 PM


A function to create a classic X-Y-Plot or scatter plot. Each data point is plotted as a separate point. The points are not connected.


This plot function is useful whenever you want to plot two parameters against each other. Additionally, the points can be colored be one of those two parameters or by another third parameter.

How to use

First, select the dataset to visualise in the field Data.

Axes Selection

In this section you control which columns of the dataset are plotted on the two axes.

  • X Column defines the column which is plotted on the x-axis.
  • Y Column defines the column which is plotted on the y-axis.
  • If you enable the option Make Interactive you can change the columns used for each axis dynamically without re-entering the edit mode.

You can use both numerical and categorical parameters on both axes. See below for more.

Display Options

In the section Display options you are setting up the appearance of the plot.

  • Marker colors is an optional parameter. If nothing is selected here all data points will be plotted with a single color (red). You can select one of the columns in the dataset which then will be used to color the data points. The column used for coloring can be numerical or categorical. More on some specifics of this option see below.
  • Hover-over text is also optional. When hovering over a data point in the final plot, information on that data point is printed in a pop-up box. The value of any column added here will be plotted. That way the value of a certain column for each data point can be assessed. If this field is left empty only the x- and y-value of the data point is printed.
  • Marker size defines how big each data point is plotted. The bigger the number of data points that are plotted the smaller this value should be chosen. The default value is 6.
  • Marker symbol defines which symbol is used to plot the data points. You can choose among a wide range of options. By default a circle is used.

Click Apply to render the plot.


Plotting aerodynamics results: Drag coefficient of a design versus the lift coefficient. The data is colored by the angle of attack of the incoming flow.

Plotting an engine map: Engine torque versus engine speed. The data is colored by NOx emissions for each operating condition.

Plotting time series: Track dynamics data, force in z-direction at front wheel versus time. Data from multiple driving manoeuvres is plotted, each with a distinct color.

Note: as the sampling frequency is quite high the plot appears to be a line plot. But it is actually a 2D point plot!

More on this step

Using categorical columns for an axis

You can use both numerical and categorical data on either axis. If you use categorical data, the data will get sorted by the distinct categorical values similar to a bar plot. The effect is similar to a box and whiskers plot but without the statistical attributed added. Here’s an example with the dataset from Challenge 1 - Composite Materials (dataset was reduced to increase clarity):

The initial stiffness of the composite material is plotted versus the carbon fiber used. The points are stacked according to carbon fibers, the categorical data is sorted alpha-numerically. You easily see the stiffness range for each fiber. But compared to Box & Whiskers the actual distribution doesn't become really clear and the statistics are missing.

How the data type drives Marker colors (color by)

  • If you select a numerical column in the Marker colors field, a color range is plotted besides the actual plot.
  • If you select a categorical column a legend with a list of all categories is plotted besides the plot. Each category will be marked by a distinct color. This works for a maximum of up to 12 categories. If more than 12 categories are present in the selected column all categories in excess of 12 will be accumulated as “other” with a single color. The 12 categories which get distinct colors are selected randomly and can’t controlled by the user. It is therefore recommended to reduce the dataset to maximal 12 categories if possible.
  • If not all categories are plotted an info message appears above the plot
  • In the section above you see an example for a plot colored by a categorical parameter (points are colored by carbon fiber).

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