Styling Custom Plot Settings¶
In the last tutorial, we saw how Vis
objects could be exported into visualization code for further editing. What if we want to change the chart settings for all the visualizations displayed in the widget. In Lux, we can change the chart settings and aesthetics by inputting custom plot settings the plot_config
property of the dataframe.
Example #1 : Changing Color and Title of all charts¶
Here, we’ve loaded in the Cars dataset and the visualizations recommended in the widget are in its default settings.
df
By default, visualizations in Lux are rendered using the Altair library. To change the plot configuration in Altair, we need to specify a function that takes an AltairChart as input, performs some chart configurations in Altair, and returns the chart object as an output.
Let’s say that we want to change all the graphical marks of the charts to green and add a custom title. We can define this change_color_add_title function, which configures the chart’s mark as green and adds a custom title to the chart.
def change_color_add_title(chart):
chart = chart.configure_mark(color="green") # change mark color to green
chart.title = "Custom Title" # add title to chart
return chart
We then set the plot configuration of the dataframe by changing the plot_config
property. With the added plot_config, Lux runs this user-defined function after every Vis is rendered to a chart, allow the user-defined function to override any existing default chart settings.
df.plot_config = change_color_add_title
We now see that the displayed visualizations adopt these new imported settings.
If we click on the visualization for Displacement v.s. Weight and export it. We see that the exported chart now contains code with these additional plot settings at the every end.
# Before running this cell, click on Displacement v.s. Weight vis and export it.
vis = df.exported[0]
print (vis.to_Altair())
import altair as alt
chart = alt.Chart(df).mark_circle().encode(
x=alt.X('Weight',scale=alt.Scale(domain=(1613, 5140)),type='quantitative'),
y=alt.Y('Displacement',scale=alt.Scale(domain=(68.0, 455.0)),type='quantitative')
)
chart = chart.configure_mark(tooltip=alt.TooltipContent('encoding')) # Setting tooltip as non-null
chart = chart.interactive() # Enable Zooming and Panning
chart = chart.configure_title(fontWeight=500,fontSize=13,font='Helvetica Neue')
chart = chart.configure_axis(titleFontWeight=500,titleFontSize=11,titleFont='Helvetica Neue',
labelFontWeight=400,labelFontSize=8,labelFont='Helvetica Neue',labelColor='#505050')
chart = chart.configure_legend(titleFontWeight=500,titleFontSize=10,titleFont='Helvetica Neue',
labelFontWeight=400,labelFontSize=8,labelFont='Helvetica Neue')
chart = chart.properties(width=160,height=150)
chart = chart.configure_mark(color="green") # change mark color to green
chart.title = "Custom Title" # add title to chart
chart
Example #2: Changing Selected Chart Setting¶
Next, we look at an example of customizing the chart setting for only selected sets of visualizations.
Here, we load in the Olympics dataset and see that the recommended visualization is cluttered with many datapoints.
df = pd.read_csv("../../lux/data/olympic.csv")
df["Year"] = pd.to_datetime(df["Year"], format='%Y') # change pandas dtype for the column "Year" to datetype
df.default_display = "lux"
df
We want to decrease the opacity of scatterplots, but keep the opacity for the other types of visualization as default.
def changeOpacityScatterOnly(chart):
if chart.mark=='circle':
chart = chart.configure_mark(opacity=0.1) # lower opacity
return chart
df.plot_config = changeOpacityScatterOnly
df
We can modify the scatterplot setting, without changing the settings for the other chart types.