Matplotlib Venn

1.1.2 · active · verified Thu Apr 16

matplotlib-venn is a Python library that provides functions for plotting area-proportional two- and three-way Venn diagrams using Matplotlib. It enables visualization of set relationships with circles whose areas correspond to subset sizes. The current stable version is 1.1.2. The library sees active maintenance, with updates addressing functionality and dependencies, though not on a rapid fixed schedule.

Common errors

```
ModuleNotFoundError: No module named 'matplotlib_venn'
```
cause The package was either not installed, or installed in a different environment, or an incorrect import path was used (e.g., trying to import `matplotlib-venn` directly instead of `matplotlib_venn`). This often occurs when using `conda install matplotlib-venn` without specifying the `conda-forge` channel.

fix Ensure the package is installed in the active environment via `pip install matplotlib-venn` or, for Conda users, `conda install -c conda-forge matplotlib-venn`. Verify the import statement: `from matplotlib_venn import venn2` (note the underscore).
```
Venn diagram does not display or appear in Jupyter/script output.
```
cause In non-interactive environments (like scripts) or Jupyter Notebooks, Matplotlib figures require an explicit command to be shown or saved. Without `plt.show()` or saving the figure, the plot is generated but not rendered.

fix Always include `import matplotlib.pyplot as plt` and `plt.show()` at the end of your plotting code to display the generated Venn diagram. In Jupyter, ensure `%matplotlib inline` or `%matplotlib notebook` is set if interactive plots are desired.
```
Distorted or poorly laid out venn3 diagram, especially with zero or very small subset values.
```
cause The mathematical complexity of area-proportional three-circle Venn diagrams can lead to suboptimal layouts or even regions not appearing when subset sizes are zero or extremely disproportionate, as the algorithm struggles to balance all constraints.

fix Consider 'regularizing' the subset areas by adding a small constant value to all regions, especially if some are zero. If area proportionality is critical and visuals are distorted, manually adjust label positions (`v.get_label_by_id().set_position()`) or consider alternative visualization methods for more complex set relationships.

Warnings

gotcha For three-circle Venn diagrams (`venn3`), it is not always mathematically possible to achieve exact correspondence between required subset sizes and region areas due to the geometric constraints of circles. The layout algorithm prioritizes accurate representation of overall set and pairwise intersection areas.
Fix: Be aware that visual proportionality for all 7 regions in `venn3` might not be perfect, especially with highly disparate subset sizes. For exact representation, consider alternative visualizations or other libraries like `matplotlib_set_diagrams` or `supervenn` for more than three sets.
deprecated The `shapely` package, currently an optional dependency for the 'cost-based' layout algorithm in `venn3` (since version 1.1.0), is likely to become a required dependency in a future version. This could lead to breaking changes if not installed proactively.
Fix: It is recommended to install `matplotlib-venn` with the `shapely` extra: `pip install "matplotlib-venn[shapely]"` to ensure access to all features and prepare for future dependency changes.
gotcha When `set_labels` are not explicitly provided to `venn2` or `venn3`, the diagrams will default to generic labels like 'A', 'B', 'C'. This can lead to misleading or uninformative plots, especially in a data analysis context.
Fix: Always provide meaningful labels for your sets using the `set_labels` parameter, e.g., `venn2(subsets=(3,2,1), set_labels=('Set of Apples', 'Set of Bananas'))`.

Install

pip install matplotlib-venn Standard installation
pip install "matplotlib-venn[shapely]" With optional shapely dependency for improved venn3 layout
conda install -c conda-forge matplotlib-venn Conda installation via conda-forge

Imports

venn2
```
from matplotlib_venn import venn2
```

venn2_circles

from matplotlib_venn import venn2_circles

venn3
```
from matplotlib_venn import venn3
```

venn3_circles

from matplotlib_venn import venn3_circles

Quickstart

This example demonstrates how to create both two-set and three-set Venn diagrams. The two-set diagram uses explicit subset counts, while the three-set diagram uses actual set objects for input. Both examples utilize `pyplot.show()` to display the generated diagrams.

from matplotlib_venn import venn2
from matplotlib import pyplot as plt

# Create a two-set Venn diagram with subset sizes
venn2(subsets=(3, 2, 1), set_labels=('Group A', 'Group B'))
plt.title("Basic Two-set Venn Diagram")
plt.show()

# Create a three-set Venn diagram from sets
set1 = set(['A', 'B', 'C', 'D'])
set2 = set(['B', 'C', 'D', 'E'])
set3 = set(['C', 'D', 'E', 'F', 'G'])
venn3([set1, set2, set3], ('Set1', 'Set2', 'Set3'))
plt.title("Three-set Venn Diagram from Data")
plt.show()

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