Expanding Plot Limits with expand_limits()

When creating visualizations, you might occasionally need to adjust your plot boundaries to encompass specific data points or values. This is where the expand_limits() function comes in handy. It allows you to extend the plot’s scales to include particular values, ensuring they’re visible in your visualization.

import pandas as pd

from lets_plot import *

LetsPlot.setup_html()

df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg2.csv")
print(df.shape)
df.head()

(392, 9)

	miles per gallon	number of cylinders	engine displacement (cu. inches)	engine horsepower	vehicle weight (lbs.)	time to accelerate (sec.)	model year	origin of car	vehicle name
0	18.0	8	307.0	130	3504	12.0	70	US	chevrolet chevelle malibu
1	15.0	8	350.0	165	3693	11.5	70	US	buick skylark 320
2	18.0	8	318.0	150	3436	11.0	70	US	plymouth satellite
3	16.0	8	304.0	150	3433	12.0	70	US	amc rebel sst
4	17.0	8	302.0	140	3449	10.5	70	US	ford torino

p1 = ggplot(df, aes("miles per gallon", "vehicle weight (lbs.)")) + geom_point()   
p1

Expand Lower Limit Along the x-axis

p1 + expand_limits(x=0)

Expand Limits Along the y-axis

p1 + expand_limits(y=[1000, 9000])

Expand Lower Limits Along Both Axes

p1 + expand_limits(x=0, y=0)

Expanding Color-scale Limits

# Add color mapping
p2 = p1 + aes(color="number of cylinders")

gggrid([
    p2,
    # Expand the color-scale limits.
    p2 + expand_limits(color=range(2, 11, 2))
])