A density plot (also known as Kernel Density Estimation or KDE plot) visualizes the distribution of a continuous variable by smoothing the data into a continuous probability density curve. Unlike histograms which use discrete bins, density plots provide a smooth representation of the underlying distribution, making it easier to identify patterns such as skewness, modality, and overall shape.
""" anyplot.ai
density-basic: Basic Density Plot
Library: plotly 6.7.0 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-30
"""
import os
import sys
# Remove the script's own directory from sys.path to avoid shadowing the plotly package
sys.path = [p for p in sys.path if os.path.abspath(p) != os.path.dirname(os.path.abspath(__file__))]
import numpy as np
import plotly.graph_objects as go
from scipy.stats import gaussian_kde
THEME = os.getenv("ANYPLOT_THEME", "light")
# Theme-adaptive chrome tokens (see default-style-guide.md)
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"
GRID = "rgba(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
# Imprint palette — first series is always #009E73
BRAND = "#009E73"
FILL = "rgba(0,158,115,0.2)"
# Data — SAT Math scores with bimodal distribution
np.random.seed(42)
sat_scores = np.concatenate(
[
np.random.normal(540, 60, 350), # main group
np.random.normal(680, 35, 150), # high achievers
]
)
sat_scores = np.clip(sat_scores, 200, 800)
# KDE via scipy
kde = gaussian_kde(sat_scores)
x_grid = np.linspace(350, 800, 500)
density = kde(x_grid)
# Locate the two peaks (valley near 620)
split = int(500 * (620 - 350) / (800 - 350))
peak1_idx = np.argmax(density[:split])
peak2_idx = split + np.argmax(density[split:])
peak1_x, peak1_y = x_grid[peak1_idx], density[peak1_idx]
peak2_x, peak2_y = x_grid[peak2_idx], density[peak2_idx]
fig = go.Figure()
# KDE curve with filled area
fig.add_trace(
go.Scatter(
x=x_grid,
y=density,
mode="lines",
fill="tozeroy",
fillcolor=FILL,
line={"color": BRAND, "width": 2.5},
name="Density",
hovertemplate="Score: %{x:.0f}<br>Density: %{y:.4f}<extra></extra>",
)
)
# Rug plot — individual observations as tick marks along x-axis
fig.add_trace(
go.Scatter(
x=sat_scores,
y=np.zeros(len(sat_scores)),
mode="markers",
marker={"symbol": "line-ns", "size": 10, "color": INK_SOFT, "opacity": 0.5, "line": {"width": 1}},
name="Observations",
hovertemplate="Score: %{x:.0f}<extra></extra>",
)
)
# Peak annotations highlighting the bimodal structure
for label, px, py, ax, ay in [
(f"<b>Primary Peak</b><br>~{peak1_x:.0f} pts", peak1_x, peak1_y, -80, -50),
(f"<b>High Achievers</b><br>~{peak2_x:.0f} pts", peak2_x, peak2_y, 80, -40),
]:
fig.add_annotation(
x=px,
y=py,
text=label,
showarrow=True,
arrowhead=2,
arrowsize=1.2,
arrowwidth=1.5,
arrowcolor=BRAND,
font={"size": 10, "color": INK},
ax=ax,
ay=ay,
bgcolor=ELEVATED_BG,
borderpad=4,
borderwidth=0,
)
fig.update_layout(
autosize=False,
title={
"text": "density-basic · python · plotly · anyplot.ai",
"font": {"size": 16, "color": INK},
"x": 0.5,
"xanchor": "center",
},
xaxis={
"title": {"text": "SAT Math Score (points)", "font": {"size": 12, "color": INK}},
"tickfont": {"size": 10, "color": INK_SOFT},
"showgrid": False,
"zeroline": False,
"linecolor": INK_SOFT,
"showspikes": True,
"spikemode": "across",
"spikethickness": 1,
"spikecolor": "rgba(0,158,115,0.3)",
"spikedash": "dot",
},
yaxis={
"title": {"text": "Density", "font": {"size": 12, "color": INK}},
"tickfont": {"size": 10, "color": INK_SOFT},
"gridcolor": GRID,
"gridwidth": 1,
"linecolor": INK_SOFT,
"zeroline": False,
"rangemode": "tozero",
},
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
font={"color": INK},
showlegend=False,
hovermode="x",
margin={"l": 80, "r": 40, "t": 80, "b": 60},
)
fig.write_image(f"plot-{THEME}.png", width=800, height=450, scale=4)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn", config={"displayModeBar": True, "scrollZoom": True})
Part of Basic Density Plot on anyplot.ai.