Visualize output data
Add viewers for dataframe
You can specify viewers to review output dataframes in a human-friendly way. Each dataframe parameter may have a list of viewers.
You may see all available viewers opening demo dataframe and opening the toolbox on the left.
Fantastic viewers and where to find them
The following code adds Scatter plot
and Line chart viewers on the input dataframe.
- Result
- Code
#name: Adding viewer on output dataframe
#language: python
#sample: demog.csv
#input: dataframe inputDF
#output: dataframe outputDF { viewer: Line chart | Scatter plot }
outputDF = inputDF.copy()
The default script view supports viewers for output dataframes only. You may specify viewers input dataframes using advanced UI editor
Customize viewers for dataframe
Each viewer has a list of customizable properties. They control how the viewer is rendered and how it behaves. For instance, you may specify the dataframe column used as the X-axis on the scatter plot.
The list of available properties differs for each type of viewer.
You may right-click on the viewer and select Properties item in the context menu.
In the viewer tag, you can specify any property listed in the opened property panel.
You should enter the viewer property in camelCase format.
For example, here "Show regression line" property
of the scatterplot becomes showRegressionLine.
For example, the following code:
- specifies marker type and size for linechart
- enables regression line rendering for scatterplot
- Result
- Code
#name: Viewers customization
#language: python
#sample: cars.csv
#input: dataframe inputDF
#output: dataframe outputDF { viewer: Scatter plot(y: "model", markerType: star, markerSize: 15) | Scatter plot(showRegressionLine: true) }
outputDF = inputDF.copy()
The default script view shows all viewers at once. You may divide viewers by different tabs using advanced UI editor.