The same plot in 14 other libraries — Python: Bokeh, lets-plot, Matplotlib, Plotly, plotnine, Pygal, Seaborn; R: ggplot2; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts. Compare all 15 side by side: Logistic Regression Curve Plot in Python, R, Julia and JavaScript.
A logistic regression visualization showing the characteristic S-shaped (sigmoid) probability curve for binary classification. The plot displays data points colored by their binary class, the fitted logistic curve representing predicted probabilities, confidence intervals around the curve, and a dashed decision threshold line at probability 0.5. This visualization is essential for understanding how a logistic model maps continuous input features to class probabilities.
""" anyplot.ai
logistic-regression: Logistic Regression Curve Plot
Library: altair 6.1.0 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-18
"""
import os
import sys
import numpy as np
import pandas as pd
# Work around filename shadowing the altair library
sys.path.pop(0)
import altair as alt
# Theme configuration
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
BRAND = "#009E73" # First series (Fail)
SECONDARY = "#C475FD" # Second series (Pass)
# Data - Study hours vs exam pass/fail
np.random.seed(42)
n_samples = 150
# Generate study hours with different distributions for pass/fail
hours_fail = np.random.normal(3, 1.5, 60)
hours_pass = np.random.normal(7, 1.5, 90)
hours = np.concatenate([hours_fail, hours_pass])
hours = np.clip(hours, 0.5, 10)
outcome = np.concatenate([np.zeros(60), np.ones(90)])
# Fit logistic regression using gradient descent
X_b = np.column_stack([np.ones(n_samples), hours])
w = np.zeros(2)
for _ in range(1000):
z = X_b @ w
predictions = 1 / (1 + np.exp(-z))
gradient = X_b.T @ (predictions - outcome) / n_samples
w -= 0.1 * gradient
b0, b1 = w[0], w[1]
# Generate smooth curve points
x_curve = np.linspace(0, 10.5, 200)
y_proba = 1 / (1 + np.exp(-(b0 + b1 * x_curve)))
# Calculate confidence intervals
se = np.sqrt(y_proba * (1 - y_proba) / n_samples) * 2.5
ci_lower = np.clip(y_proba - 1.96 * se, 0, 1)
ci_upper = np.clip(y_proba + 1.96 * se, 0, 1)
# Create curve DataFrame
curve_df = pd.DataFrame({"Study Hours": x_curve, "Probability": y_proba, "CI Lower": ci_lower, "CI Upper": ci_upper})
# Add jitter to data points for visibility
jitter = np.random.uniform(-0.03, 0.03, len(outcome))
y_jittered = outcome + jitter
# Create data points DataFrame
points_df = pd.DataFrame(
{
"Study Hours": hours,
"Outcome": outcome,
"Outcome Jittered": y_jittered,
"Class": ["Fail" if o == 0 else "Pass" for o in outcome],
}
)
# Decision threshold line
threshold_df = pd.DataFrame({"Study Hours": [0, 10.5], "Probability": [0.5, 0.5]})
# Create the confidence interval band
ci_band = (
alt.Chart(curve_df)
.mark_area(opacity=0.25, color=INK_SOFT)
.encode(x=alt.X("Study Hours:Q"), y=alt.Y("CI Lower:Q"), y2=alt.Y2("CI Upper:Q"))
)
# Create the logistic curve
curve = (
alt.Chart(curve_df).mark_line(strokeWidth=4, color=INK).encode(x=alt.X("Study Hours:Q"), y=alt.Y("Probability:Q"))
)
# Create the data points with Okabe-Ito colors
points = (
alt.Chart(points_df)
.mark_circle(size=200, opacity=0.6, strokeWidth=1, stroke=PAGE_BG)
.encode(
x=alt.X("Study Hours:Q", title="Study Hours (hrs)", scale=alt.Scale(domain=[0, 10.5])),
y=alt.Y("Outcome Jittered:Q", title="Probability", scale=alt.Scale(domain=[-0.05, 1.05])),
color=alt.Color(
"Class:N",
scale=alt.Scale(domain=["Fail", "Pass"], range=[BRAND, SECONDARY]),
legend=alt.Legend(
title="Exam Result",
titleFontSize=20,
labelFontSize=18,
symbolSize=300,
fillColor=ELEVATED_BG,
strokeColor=INK_SOFT,
),
),
tooltip=["Study Hours", "Class"],
)
)
# Decision threshold line
threshold = (
alt.Chart(threshold_df)
.mark_line(strokeDash=[12, 8], strokeWidth=3, color=INK_SOFT)
.encode(x=alt.X("Study Hours:Q"), y=alt.Y("Probability:Q"))
)
# Threshold label
threshold_label = (
alt.Chart(pd.DataFrame({"x": [9.5], "y": [0.54], "text": ["Decision Threshold (p=0.5)"]}))
.mark_text(fontSize=16, color=INK_SOFT, align="right")
.encode(x="x:Q", y="y:Q", text="text:N")
)
# Combine all layers
chart = (
alt.layer(ci_band, curve, threshold, threshold_label, points)
.properties(
width=1600,
height=900,
background=PAGE_BG,
title=alt.Title("logistic-regression · python · altair · anyplot.ai", fontSize=28, anchor="middle", color=INK),
)
.configure_axis(
labelFontSize=18,
titleFontSize=22,
gridOpacity=0.15,
domainColor=INK_SOFT,
tickColor=INK_SOFT,
gridColor=INK_SOFT,
labelColor=INK_SOFT,
titleColor=INK,
)
.configure_view(strokeWidth=0, fill=PAGE_BG)
.configure_title(color=INK)
)
# Save outputs
chart.save(f"plot-{THEME}.png", scale_factor=3.0)
chart.save(f"plot-{THEME}.html")
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/logistic-regression/altair/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "logistic-regression",
"language": "python",
"library": "altair",
"page": "https://anyplot.ai/logistic-regression/python/altair",
"hub": "https://anyplot.ai/logistic-regression",
"code_json": "https://api.anyplot.ai/specs/logistic-regression/altair/code",
"spec_json": "https://api.anyplot.ai/specs/logistic-regression",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/altair/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/altair/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/altair/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/altair/plot-dark.html",
"quality_score": 89.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Logistic Regression Curve Plot on anyplot.ai.