statanim 0.1.3

A Manim extension for animated statistical visualisations — distributions, regression, probability, inference and more.

A Manim extension for animated statistical visualisations.

Distributions, inference, regression, probability theory, and physical props — all built on Manim Community.

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Why statanim?

Manim is extraordinary at animating mathematics — but when it comes to statistics, you're on your own. Want to show a normal distribution shading its tails as sigma changes? A probability tree that builds itself node by node? A deck of cards dealing out to demonstrate hypergeometric sampling? With vanilla Manim, each of these requires hundreds of lines of low-level geometry code before you even start on the actual statistics.

statanim closes that gap. It gives statistics the same treatment Manim gives mathematics: objects that understand what they represent, animations that carry statistical meaning, and a workflow that stays out of your way so you can focus on the idea you're trying to communicate.

Whether you're building lecture slides, making YouTube explainers, or just trying to build intuition for a concept — statanim lets you go from idea to animation in minutes, not days.

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Demo

Sample Space

Classical Probability

Conditional Probability

Hypergeometric Distribution

Birthday Paradox

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Installation

Prerequisites

pip install manim

LaTeX is required for formula rendering:

# macOS
brew install mactex-no-gui

# Ubuntu / Debian
sudo apt-get install texlive-full

# Windows: install MiKTeX from https://miktex.org/

Install statanim

pip install statanim

Install from source

git clone https://github.com/rishabhbhartiya/statanim.git
cd statanim
pip install -e .

Verify

from manim import *
from statanim.props.card import Card3D
from statanim.distributions.normal3d import NormalCurve3D
from statanim.core.colors import DARK_THEME

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Quick Start

Normal distribution with shaded region

from manim import *
from statanim.distributions.normal3d import NormalCurve3D

class NormalDemo(Scene):
    def construct(self):
        axes = Axes(x_range=[-4, 4, 1], y_range=[0, 0.45, 0.1],
                    x_length=8, y_length=4)
        curve = NormalCurve3D(mu=0, sigma=1, axes=axes)

        self.play(Create(axes))
        self.play(Create(curve))
        self.play(curve.shade_region(x_min=-1, x_max=1))
        self.wait(2)

Probability with playing cards

from manim import *
from statanim.props.card import Deck3D, CardFacing, standard_deck

class CardDraw(Scene):
    def construct(self):
        deck = Deck3D(cards=standard_deck(shuffle=True, seed=0),
                      initial_facing=CardFacing.FACE_DOWN)
        deck.move_to(LEFT * 3)
        self.play(FadeIn(deck))
        self.play(deck.deal_one(target=RIGHT * 2, flip=True))
        self.wait(2)

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What's Inside

Module	What it gives you
distributions	Normal, Binomial, Poisson, Hypergeometric, Beta, Gamma, Chi-squared, Student-t, F — all with PDF/PMF/CDF visualisers and shadeable regions
charts	BarChart3D, Histogram3D, BoxPlot3D, ViolinPlot3D, HeatMap3D, ScatterPlot3D, LinePlot3D — statistically correct, 3D-aware
probability	SampleSpace3D, VennDiagram3D, ProbabilityTree3D, BayesBox3D — builds event regions and conditional flows step by step
inference	HypothesisRegion3D, ConfidenceInterval3D, SamplingDistribution3D, Type I/II error visualisation
regression	RegressionPlane3D, ScatterCloud3D, residual diagnostics, correlation matrices, CI/PI shells
props	Card3D, Deck3D, Coin3D, Die3D (D4–D20), Spinner3D, Urn3D — physical probability objects with built-in logic and animations
animations	CLTDemo, DistMorph3D, ParameterSweep3D, BootstrapSampling3D, ScatterToRegression3D
ui	Ticker3D, PValueTicker3D, FormulaPanel3D, DataTable3D, AnnotationArrow3D
core	6 built-in themes (DARK, LIGHT, PAPER, and more), diverging colormaps, 50+ LaTeX formula builders

Full class and parameter documentation: API_REFERENCE.md

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Camera Orientation Reference

All ThreeDScene subclasses need an explicit camera orientation at the top of construct().

def construct(self):
    self.set_camera_orientation(phi=70*DEGREES, theta=-45*DEGREES)

Scene type	phi	theta
3D bar chart	65°	-55°
Regression plane	70°	-45°
Scatter cloud	70°	-60°
Card grid (table-top)	60°	-45°
Bivariate normal surface	68°	-45°

Use Scene (not ThreeDScene) for 2D content: PDF curves, PMF bars, Venn diagrams, histograms, box plots, probability trees, and hypothesis test plots.

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Development Setup

git clone https://github.com/rishabhbhartiya/statanim.git
cd statanim
python -m venv .venv
source .venv/bin/activate       # Windows: .venv\Scripts\activate
pip install -e ".[dev]"

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Contributing

Issues and pull requests are welcome at github.com/rishabhbhartiya/statanim.

When adding a new distribution, inherit from BaseDistribution3D, implement pdf/pmf, cdf, mean, variance, and add a scene in scenes/demo_distributions.py.

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Acknowledgements

Built on Manim Community. Statistical algorithms from SciPy and NumPy. Inspired by the mathematical animation work of 3Blue1Brown.

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License

MIT License. See LICENSE for details.

Author: Rishabh Bhartiya — [e-mail]