Generate publication-ready survival curves with log-rank tests, risk tables, confidence intervals, and censoring marks using Python and matplotlib.
Survival analysis tracks time-to-event data - from patient mortality in clinical trials to equipment failure in engineering. The Kaplan-Meier estimator is the standard non-parametric approach, with several complementary visualizations used in published research.
Treatment vs control with log-rank p-value and confidence bands
Three or more cohort curves with pairwise significance tests
Number of subjects remaining at risk beneath the curve at each time point
95% CI bands using Greenwood formula to quantify estimation uncertainty
Nelson-Aalen estimator as complement to survival probability
RMST summary statistic at a landmark time for clinical reporting
Pure numpy/matplotlib implementation with manual Kaplan-Meier, Greenwood CI, and log-rank testing - no extra packages needed.
Automatic log-rank p-value printed on the curve for two or more groups with multivariate support.
Tick marks placed at censored observations so reviewers see the complete dataset at a glance.
Spine cleanup, Arial font, 300 DPI export ready for journal submission to Nature Medicine or NEJM.
Two-group Kaplan-Meier with log-rank test and censoring tick marks. Edit the exponential scale parameters to model faster or slower event rates in your treatment arms.
Preparing preview
Running once automatically on first load
This is a safe playground for learning! Try changing:
Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
Try with your data
These biology datasets work well as demo data for survival-style analysis. Upload your own time-to-event CSV for real survival curves.
Dose-escalation trial with Placebo, Low Dose, and High Dose arms. Panel A shows KM survival curves with multivariate log-rank test. Panel B shows the Nelson-Aalen cumulative hazard as a complement.
Preparing preview
Running once automatically on first load
This is a safe playground for learning! Try changing:
Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
Chart gallery
Interactive examples with ready-to-run code
Combines box plots with kernel density to show distribution shape across groups.
Sample code / prompt
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from scipy.stats import f_oneway
# Generate exam score data for 3 groups
np.random.seed(42)
control = np.random.normal(72, 12, 50)
treatment_a = np.random.normal(78, 10, 50)
Represents data values as colors in a two-dimensional matrix format.
Sample code / prompt
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# Create correlation matrix for financial metrics
metrics = ['Revenue', 'Profit', 'Expenses', 'ROI', 'Customers', 'AOV', 'Marketing', 'Employees']
correlation_data = np.array([
[1.00, 0.85, -0.45, 0.72, 0.88, 0.65, 0.72, 0.55],
[0.85, 1.00, -0.78, 0.92, 0.75, 0.58, 0.63, 0.48],Upload your time-to-event CSV, describe the survival plot you need, and get publication-ready figures with full Python code in seconds.
Start Free - No Sign-Up