potatonumpy 0.1.0

A pure Python linear algebra and tensor library for educational purposes

PotatoNumPy 🥔

A pure Python linear algebra and tensor library built entirely from scratch. No NumPy, no C extensions, no shortcuts — just potatoes all the way down.

It took me 3 weeks alone and 2 hours of Claude documentation refinement to make this.

What is this?

PotatoNumPy is a learning project that answers three questions I kept asking myself:

1. How does linear algebra actually work under the hood? Every matrix multiplication here is three nested loops. Every determinant is recursive cofactor expansion. Nothing hides behind an opaque C function — you can read every line and follow along.

2. Why is NumPy so ridiculously fast? After watching PotatoNumPy chug through a 50x50 matrix multiply, you'll feel what NumPy's C/Fortran backends, SIMD vectorization, and contiguous memory layouts buy you. It's not subtle.

3. What does Python's interpreter overhead actually cost? Dynamic type checking, function call overhead, pointer-chasing through Python lists — it all adds up. This library makes that cost painfully tangible.

Installation

pip install potatonumpy

Or clone and install locally:

git clone https://github.com/Madhavyamjala/potatonumpy.git
cd potatonumpy
pip install -e .

No dependencies needed — that's the whole point.

Quick Start

import potatonumpy as pp

# Create arrays
a = pp.array([1, 2, 3])
b = pp.array([4, 5, 6])

# Elementwise math, just like you'd expect
print(a + b)        # [5, 7, 9]
print(a * b)        # [4, 10, 18]
print(a ** 2)       # [1, 4, 9]
print(a + 10)       # [11, 12, 13]

# Linear algebra
print(pp.dot(a, b))         # 32
print(pp.cross(a, b))       # [-3, 6, -3]
print(pp.magnitude(a))      # 3.7416...
print(pp.normalize(a))      # [0.2672, 0.5345, 0.8017]

Matrix Operations

import potatonumpy as pp

A = pp.array([[1, 2], [3, 4]])
B = pp.array([[5, 6], [7, 8]])

print(pp.matmul(A, B))      # [[19, 22], [43, 50]]
print(pp.transpose(A))      # [[1, 3], [2, 4]]
print(pp.determinant(A))    # -2.0
print(pp.inverse(A))        # [[-2, 1], [1.5, -0.5]]
print(pp.trace(A))          # 5
print(pp.diagonal(A))       # [1, 4]

# Special matrices
print(pp.identity(3))
print(pp.zeros((2, 3)))

Array Manipulation & Attributes

You can inspect array metadata and modify structural dimensions.

import potatonumpy as pp

a = pp.array([[1, 2, 3], [4, 5, 6]])

print(a.ndim)
print(a.size)
print(a.shape)

flat = a.flatten()
print(flat)

reshaped = flat.reshape((3, 2))
print(reshaped)

raw_list = reshaped.tolist()
print(raw_list)

Tensors? Yeah, We Got Those

import potatonumpy as pp

# 3D tensor — go wild
t = pp.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
print(t.shape)              # (2, 2, 2)

# Reductions
print(pp.tensor_sum(t))             # 36
print(pp.tensor_sum(t, axis=0))     # [[6, 8], [10, 12]]
print(pp.tensor_mean(t))            # 4.5
print(pp.tensor_min(t))             # 1
print(pp.tensor_max(t))             # 8

Type Support

Works with int, float, and complex:

c = pp.array([1+2j, 3+4j])
print(c + 1)    # [(2+2j), (4+4j)]

Error Handling

PotatoNumPy doesn't silently do the wrong thing. It yells at you with specific exceptions:

from potatonumpy import (
    ShapeMismatchError,  # tried to add (2,) and (3,)?
    InvalidTensorError,  # ragged array? nope
    SingularMatrixError,  # can't invert that matrix, sorry
    InvalidOperationError,  # division by zero, etc.
)

pp.array([1, 2]) + pp.array([1, 2, 3])  # ShapeMismatchError
pp.array([[1, 2], [3, 4, 5]])  # InvalidTensorError
pp.inverse(pp.array([[1, 2], [2, 4]]))  # SingularMatrixError
pp.array([1, 2]) / 0  # InvalidOperationError

Benchmarks (Prepare to Cringe)

python examples/benchmarking.py

Here's what you'll see (roughly):

Operation	Pure Loops	PotatoNumPy	NumPy	Slowdown vs NumPy
Vector Add (10k)	~2ms	~5ms	~0.01ms	~500x
Matrix Mul (50x50)	~50ms	~80ms	~0.05ms	~1600x
Scalar Mul (10k)	~1ms	~3ms	~0.005ms	~600x

Yeah. NumPy is that much faster. Here's why:

What	PotatoNumPy	NumPy
Inner loops	Python bytecode	Compiled C/Fortran
Memory layout	Scattered Python objects	Contiguous typed arrays
Vectorization	Nope	SIMD instructions
Type checking	Every single operation	Once at array creation
Function calls	Python stack frames	Inlined C calls
Math backend	Hand-rolled loops	BLAS/LAPACK

Running Tests

python -m unittest discover tests -v

99 tests (One Short 🥲) covering all operations, edge cases, and error conditions.

Project Structure

.
├── src/
│   └── potatonumpy/
│       ├── __init__.py       
│       ├── core.py           
│       ├── linalg.py         
│       ├── tensor.py         
│       ├── exceptions.py     
│       ├── utils.py          
│       └── benchmarks.py     
├── tests/
│   ├── test_core.py
│   ├── test_linalg.py
│   └── test_tensor.py
├── examples/
│   ├── vectors.py
│   ├── matrices.py
│   └── benchmarking.py
├── README.md
├── pyproject.toml
└── setup.py

Design Philosophy

• No magic. Every operation is explicit loops you can step through in a debugger. Want to understand how matrix inverse works? Read the code.
• Recursion over cleverness. Determinants use cofactor expansion. Shape validation walks the full tree. This is intentional — clarity over performance.
• The slowness is a feature. Seriously. The massive performance gap between PotatoNumPy and NumPy is the lesson.
• Zero dependencies. Standard library only. If Python doesn't ship it, we don't use it.

Limitations (by Design)

• Slow. Orders of magnitude slower than NumPy. That's the point.
• Scalar broadcasting only. No fancy NumPy-style shape broadcasting.
• Memory hungry. Python lists use ~8x more memory per element than NumPy arrays.
• Determinant is O(n!). Don't try matrices bigger than ~12x12 unless you have time to spare.
• No eigenvalues, SVD, FFT, or sparse matrices. This is an educational tool, not a production library.

License

MIT License. See LICENSE for details.

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Built with love, frustration, and an unreasonable number of nested for loops. 🥔