Basic Raincloud Plot — plotnine

A raincloud plot combines three visualization elements—a half-violin (the "cloud"), jittered strip points (the "rain"), and a box plot—to provide a comprehensive view of data distribution. This hybrid approach shows distribution shape, summary statistics, and individual observations simultaneously, addressing the limitation of box plots that can hide multimodal distributions. Often called the "gold standard" for transparent statistical visualization in scientific publications.

Basic Raincloud Plot rendered with plotnine

Python source (plotnine)

""" anyplot.ai
raincloud-basic: Basic Raincloud Plot
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-26
"""

import os

import numpy as np
import pandas as pd
from plotnine import (
    aes,
    annotate,
    coord_flip,
    element_blank,
    element_line,
    element_rect,
    element_text,
    geom_boxplot,
    geom_jitter,
    geom_violin,
    ggplot,
    labs,
    scale_color_manual,
    scale_fill_manual,
    scale_x_discrete,
    scale_y_continuous,
    stage,
    theme,
    theme_minimal,
)


# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"

# Imprint palette — first series ALWAYS #009E73
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]

# Data — reaction times (ms) for three experimental conditions
np.random.seed(42)
control = np.random.normal(450, 60, 80)
treatment_a = np.random.normal(380, 50, 80)
treatment_b = np.concatenate([np.random.normal(350, 40, 50), np.random.normal(500, 45, 30)])

df = pd.DataFrame(
    {
        "condition": (
            ["Control"] * len(control) + ["Treatment A"] * len(treatment_a) + ["Treatment B"] * len(treatment_b)
        ),
        "reaction_time": np.concatenate([control, treatment_a, treatment_b]),
    }
)
df["condition"] = pd.Categorical(df["condition"], categories=["Treatment B", "Treatment A", "Control"], ordered=True)

# Category → Imprint palette positions 1, 2, 3 (brand green is Control, the reference group)
colors = {"Control": IMPRINT_PALETTE[0], "Treatment A": IMPRINT_PALETTE[1], "Treatment B": IMPRINT_PALETTE[2]}

cloud_shift = 0.15

# Plot — horizontal raincloud via coord_flip
# Pre-flip: x=condition (categorical), y=reaction_time. After flip: categories on y, values on x.
plot = (
    ggplot(df, aes(x="condition", y="reaction_time", fill="condition", color="condition"))
    # Cloud (half-violin) — style="right" extends positive x = upward after flip
    + geom_violin(
        aes(x=stage("condition", after_scale="x+{0}".format(cloud_shift))),
        style="right",
        trim=True,
        scale="width",
        size=0.3,
        alpha=0.78,
        show_legend=False,
    )
    # Boxplot — centered on category baseline, theme-adaptive fill/edge
    + geom_boxplot(width=0.06, outlier_shape="", fill=ELEVATED_BG, color=INK, size=0.5, alpha=0.95, show_legend=False)
    # Rain (jittered points) — nudged negative x = downward after flip
    + geom_jitter(
        aes(x=stage("condition", after_scale="x-0.18")), width=0.06, height=0, size=3.0, alpha=0.6, show_legend=False
    )
    # Annotation: bimodal callout for Treatment B (category index 1, 0-based)
    + annotate(
        "text",
        x=0.35,
        y=425,
        label="Bimodal: two distinct response clusters",
        size=13,
        color=INK_SOFT,
        ha="center",
        fontstyle="italic",
    )
    # Arrows land inside the half-violin's two humps (above baseline x=1.15, at peak y=350 / y=500)
    + annotate("segment", x=0.5, xend=1.28, y=420, yend=350, size=0.5, color=INK_MUTED, linetype="dashed")
    + annotate("segment", x=0.5, xend=1.28, y=430, yend=500, size=0.5, color=INK_MUTED, linetype="dashed")
    # Annotation: Treatment A shifted left
    + annotate(
        "text",
        x=1.55,
        y=290,
        label="Faster responses\nvs. Control →",
        size=12,
        color=INK_SOFT,
        ha="left",
        fontstyle="italic",
    )
    + scale_fill_manual(values=colors)
    + scale_color_manual(values=colors)
    # Extra bottom padding (after coord_flip, the lower x = lower vertical) for the bimodal annotation
    + scale_x_discrete(expand=(0, 0.95, 0, 0.6))
    + scale_y_continuous(expand=(0.02, 0, 0.08, 0))
    + coord_flip()
    + labs(x="Experimental Condition", y="Reaction Time (ms)", title="raincloud-basic · python · plotnine · anyplot.ai")
    + theme_minimal()
    + theme(
        figure_size=(8, 4.5),
        text=element_text(size=7, color=INK),
        axis_title=element_text(size=10, color=INK),
        axis_text=element_text(size=8, color=INK_SOFT),
        plot_title=element_text(size=14, color=INK),
        panel_grid_major_y=element_blank(),
        panel_grid_minor=element_blank(),
        panel_grid_major_x=element_line(color=INK, size=0.3, alpha=0.15),
        panel_border=element_blank(),
        axis_line=element_line(color=INK_SOFT, size=0.4),
        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
        panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
        legend_position="none",
        plot_margin=0.02,
    )
)

# Save
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)

Part of Basic Raincloud Plot on anyplot.ai.

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