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Sign in to get your referral linkHeatmaps transform matrices of numbers into color-encoded patterns that reveal correlations, clusters, and outliers at a glance. From gene expression to survey data, Plotivy creates publication-ready heatmaps with automatic clustering.
Shows pairwise Pearson/Spearman correlations. Use triangular mask to avoid redundancy. Best for feature selection and multicollinearity detection.
Gene expression or abundance matrices with hierarchical clustering dendrograms. Standard in RNA-seq, proteomics, and microbiome studies.
Confusion matrices, contingency tables, or performance metrics with numeric annotations inside each cell. Common in ML and clinical studies.
A quick check to choose heatmaps confidently and avoid overcomplicated figures.
This code creates a lower-triangle correlation heatmap with annotated r-values, a diverging colormap, and publication-ready styling. Edit and run instantly.
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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.
RdBu_r for diverging data (correlations), viridis for sequentialdf.corr(method="spearman") for non-linear relationshipsThe gold standard for gene expression visualization. This code creates a heatmap with row and column dendrograms showing hierarchical clustering - all from scratch.
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.
Plotivy detects your data range and applies z-score normalization or min-max scaling to ensure clear color contrast.
Ward, complete, average, or single linkage clustering applied to rows and columns with dendrograms drawn automatically.
Diverging colormaps for correlations, sequential for counts, and perceptually uniform options for accessibility.
Heatmaps with 50+ features export cleanly at 300 DPI with readable labels and proper aspect ratios.
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Explore more ways to visualize matrices and patterns
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
Compares categorical data using rectangular bars with heights proportional to values.
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 performance scores for 5 treatment groups
np.random.seed(42)
groups = ['Control', 'Treatment A', 'Treatment B', 'Treatment C', 'Treatment D']
n_samples = 30
Displays multivariate data on axes starting from a central point.
Sample code / prompt
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
import pandas as pd
# EV Model comparison data (0-100 scale)
categories = ['Range', 'Acceleration', 'Charging Speed',
'Interior Quality', 'Technology', 'Value']
tesla_scores = [85, 90, 88, 70, 95, 80]
bmw_scores = [70, 80, 75, 90, 85, 65]
Displays the distribution of numerical data by grouping values into bins.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde, skewnorm
# Generate age data with slight right skew
np.random.seed(42)
ages = skewnorm.rvs(a=2, loc=42, scale=15, size=500)
ages = np.clip(ages, 18, 80) # Clip to realistic range
fig, ax = plt.subplots(figsize=(12, 7))Upload a CSV and Plotivy creates clustered, annotated heatmaps ready for your next publication.