Point Estimate Plot — Altair

A point estimate plot displays central tendency values (means, medians, or other estimates) with confidence intervals or error bars for each category. Each point represents the estimate, and the lines extending from it show the uncertainty range.

Point Estimate Plot rendered with Altair

Python source (Altair)

""" anyplot.ai
point-basic: Point Estimate Plot
Library: altair 6.1.0 | Python 3.13.13
Quality: 94/100 | Updated: 2026-05-11
"""

import os
from pathlib import Path


# Change to script directory to ensure files save in the right place
os.chdir(Path(__file__).parent)

import altair as alt
import numpy as np
import pandas as pd


# 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"

# Okabe-Ito palette - first series is always #009E73 (brand green)
BRAND = "#009E73"
SECONDARY = "#C475FD"
REFERENCE_LINE = "#888888"

# Data - Treatment effect estimates with 95% confidence intervals
np.random.seed(42)
groups = ["Treatment A", "Treatment B", "Treatment C", "Treatment D", "Control", "Placebo"]
estimates = [2.4, 1.8, 3.1, -0.5, 0.2, 0.8]
ci_widths = [0.8, 1.2, 0.6, 1.0, 0.9, 1.1]
lower = [e - w for e, w in zip(estimates, ci_widths, strict=True)]
upper = [e + w for e, w in zip(estimates, ci_widths, strict=True)]

df = pd.DataFrame({"Group": groups, "Estimate": estimates, "Lower": lower, "Upper": upper})

# Base chart for points
points = (
    alt.Chart(df)
    .mark_point(size=400, filled=True, color=BRAND)
    .encode(
        x=alt.X("Estimate:Q", title="Effect Size", scale=alt.Scale(domain=[-3, 5])),
        y=alt.Y("Group:N", title=None, sort=None),
        tooltip=["Group:N", "Estimate:Q", "Lower:Q", "Upper:Q"],
    )
)

# Error bars (confidence intervals)
error_bars = (
    alt.Chart(df)
    .mark_rule(strokeWidth=3, color=BRAND)
    .encode(x=alt.X("Lower:Q"), x2=alt.X2("Upper:Q"), y=alt.Y("Group:N", sort=None))
)

# Error bar caps (left)
caps_left = (
    alt.Chart(df).mark_tick(thickness=3, size=20, color=BRAND).encode(x=alt.X("Lower:Q"), y=alt.Y("Group:N", sort=None))
)

# Error bar caps (right)
caps_right = (
    alt.Chart(df).mark_tick(thickness=3, size=20, color=BRAND).encode(x=alt.X("Upper:Q"), y=alt.Y("Group:N", sort=None))
)

# Reference line at zero (null hypothesis)
reference_line = (
    alt.Chart(pd.DataFrame({"x": [0]}))
    .mark_rule(strokeDash=[8, 4], strokeWidth=2, color=REFERENCE_LINE)
    .encode(x=alt.X("x:Q"))
)

# Combine layers
chart = (
    alt.layer(reference_line, error_bars, caps_left, caps_right, points)
    .properties(
        width=1600,
        height=900,
        background=PAGE_BG,
        title=alt.Title("point-basic · altair · anyplot.ai", fontSize=28, anchor="middle"),
    )
    .configure_axis(
        labelFontSize=18,
        titleFontSize=22,
        labelColor=INK_SOFT,
        titleColor=INK,
        domainColor=INK_SOFT,
        tickColor=INK_SOFT,
        gridColor=INK,
        gridOpacity=0.10,
    )
    .configure_view(fill=PAGE_BG, stroke=INK_SOFT, strokeWidth=1)
    .configure_title(color=INK)
)

# Save outputs
chart.save(f"plot-{THEME}.png", scale_factor=3.0)
chart.save(f"plot-{THEME}.html")

Part of Point Estimate Plot on anyplot.ai.

Other implementations