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Sign in to get your referral linkFrom dose-response curves and growth kinetics to gene expression volcanos and survival analyses - create publication-ready biology figures with AI-generated Python code.
Biological data comes in many forms - time series from growth assays, dose-response data from drug screens, expression matrices from RNA-seq, and survival data from clinical trials. Each requires specialized plotting techniques with proper statistical overlays.
OD600 time series with lag, exponential, and stationary phase identification
4-parameter logistic fits with IC50/EC50 extraction and confidence bands
Differential expression with fold-change and significance thresholds
Gene expression matrices with hierarchical clustering and dendrograms
Kaplan-Meier estimators with log-rank tests and censoring marks
Michaelis-Menten fits, Lineweaver-Burk plots, and inhibition analysis
Biology data visualization in Python is rarely one chart and done. Most projects move through a repeatable sequence: quality-check raw measurements, compare treatment groups with uncertainty, model kinetics or dose response, then assemble publication-ready multi-panel figures.
If your dataset is categorical, start with a bar chart workflow that includes error bars and statistical overlays. If your dataset is continuous and multivariate, move to scatter plots, heatmaps, and dimensionality-reduction techniques.
For experimental biology, the highest-intent analysis steps are often technical rather than visual. Peak extraction, smoothing, ANOVA post-hoc annotation, and ROC validation are covered in the Techniques section, while full narrative tutorials live in the Blog with complete code you can adapt to your assays.
Grouped bars, ANOVA brackets, volcano plots, and reporting standards.
Grouped bars, error bars, and library-specific patterns.
Regression overlays, uncertainty, and publication formatting.
AUC, threshold selection, and model validation visuals.
4PL dose-response, Michaelis-Menten, logistic growth - statistical models generated automatically.
Volcano plots, heatmaps, and MA plots for differential expression analysis from DESeq2 or edgeR output.
Export to SVG/PDF sized for Nature, Cell, or PNAS column widths with correct DPI and fonts.
Error bars, significance brackets, p-value annotations, and confidence bands generated from data.
Three compounds screened against a cell line. The 4-parameter logistic model extracts IC50 values with confidence bands. Hover over data points in the code to modify concentrations.
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Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
Try with your data
Click any dataset to load it in the editor and generate a visualization instantly.
Bacterial growth curves for three strains modeled with logistic growth + death phase. Color-coded phase regions (lag, exponential, stationary, death) with error bars from replicate measurements.
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
Displays values for two variables as points on a Cartesian coordinate system.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import pandas as pd
# Generate sample data
np.random.seed(42)
n_samples = 200
height = np.random.normal(170, 8, n_samples)
weight = height * 0.6 + np.random.normal(0, 8, n_samples) - 50
Displays data points connected by straight line segments to show trends over time.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
# Generate temperature data for 3 major US cities over 12 months
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
nyc = [30, 32, 40, 52, 65, 75, 82, 81, 74, 63, 50, 38]
miami = [65, 66, 70, 76, 82, 87, 90, 90, 87, 80, 72, 66]
chicago = [25, 27, 35, 48, 62, 72, 80, 79, 71, 60, 45, 32]
# Create figure with enhanced styling.png&w=1280&q=70)
Displays data distribution using quartiles, median, and outliers in a standardized format.
Sample code / prompt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
# Generate gene expression data for 4 genotypes
np.random.seed(42)
genotypes = ['WT', 'KO1', 'KO2', 'Mutant']
n_per_group = 20
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],
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)Upload your CSV, describe the plot you need, and get publication-ready figures with full Python code in seconds.
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