Axis Minor Ticks¶

The axis_minor_ticks and axis_minor_ticks_length parameters in theme() control the appearance of axis minor ticks, i.e., the tick marks drawn between major ticks.

  • axis_minor_ticks - tick line attributes set via element_line()
  • axis_minor_ticks_length - tick length in pixels
  • axis_minor_ticks_x / axis_minor_ticks_y, axis_minor_ticks_length_x / axis_minor_ticks_length_y - directional overrides allow controlling only horizontal or vertical axes.

Minor ticks are placed at positions computed by the scale (e.g., midpoints between major breaks for continuous scales). They do not carry labels as for now.

In [1]:
from lets_plot import *
import numpy as np
import pandas as pd

LetsPlot.setup_html()

General Usage¶

Minor ticks can improve readability of continuous axes, especially when major breaks are sparse.

In [2]:
# Generate sample data from a normal distribution
np.random.seed(42)
x = np.random.normal(loc=0, scale=1, size=2000)
df_hist = {'x': x}

plot_norm = ggplot(df_hist) + \
    geom_histogram(aes(x='x', y='..density..'), bins=40, color='white', fill='light_sky_blue', alpha=0.7) + \
    geom_density(aes(x='x'), color='tomato', size=1.2, alpha=0) + \
    scale_x_continuous(breaks=[-3, -2, -1, 0, 1, 2, 3]) + \
    labs(title="Normal Distribution",
         x="Value",
         y="Count / Density")
plot_norm
Out[2]:
In [3]:
plot_norm + \
    ggtitle("Normal Distribution with Minor Ticks") + \
    theme(
        axis_ticks_length=8,
    
        axis_minor_ticks=element_line(color='gray85'),
        axis_minor_ticks_length=5,
    
        axis_minor_ticks_x=element_line(color='gray66', size=2),
    )
Out[3]:

Minor Ticks as Separators¶

Minor ticks can be styled as vertical separators, helping distinguish categories more clearly.

In [4]:
df = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/midwest.csv')
df["state"] = df["state"].map({
    "IL": "Illinois",
    "IN":" Indiana",
    "MI": "Michigan",
    "OH": "Ohio",
    "WI": "Wisconsin",
})
print(df.shape)

(437, 29)
In [5]:
base_plot = ggplot(df) + \
    geom_jitter(aes("state", "poptotal"), seed=42, alpha=0.3) + \
    ggtitle("Population Distribution by State") + \
    theme_gray()
base_plot
Out[5]:
In [6]:
base_plot + \
    ggtitle("Minor ticks as separators") + \
    theme(
        # disable major ticks
        axis_ticks_x=element_blank(),
        
        # and add enlarged minor ticks
        axis_minor_ticks_length_x=15.0,

        axis_minor_ticks_x=element_line(
            color="grey"
        )
    )
Out[6]: