# Sympy

> Para matemática simbólica exacta en Python: álgebra, cálculo, resolución de ecuaciones, álgebra lineal simbólica y generación de código vía lambdify/LaTeX. Si basta con aproximaciones, usa NumPy o SciPy.

Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/sympy
Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/sympy.md

Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills
Autor: K-Dense-AI
Licencia: https://github.com/sympy/sympy/blob/master/LICENSE
Actualizado: el mes pasado
Coste de contexto: 56 tok instalada, 2.5k tok al activarse, 18k tok con todos los archivos del bundle
Bundle: 7 archivos, 70 KB
Permisos que pide: read write edit bash

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add K-Dense-AI/scientific-agent-skills --skill sympy --agent claude-code
# Cursor
npx -y skills add K-Dense-AI/scientific-agent-skills --skill sympy --agent cursor
# Codex
npx -y skills add K-Dense-AI/scientific-agent-skills --skill sympy --agent codex
# Gemini CLI
npx -y skills add K-Dense-AI/scientific-agent-skills --skill sympy --agent gemini
# Windsurf
npx -y skills add K-Dense-AI/scientific-agent-skills --skill sympy --agent windsurf
# Cline
npx -y skills add K-Dense-AI/scientific-agent-skills --skill sympy --agent cline
```

## Qué hace

- Resuelve ecuaciones y sistemas de forma simbólica (algebraicas, diferenciales) con solve, solveset, linsolve, dsolve
- Realiza cálculo simbólico: derivadas, integrales, límites y series
- Manipula matrices y álgebra lineal simbólica, y calcula autovalores exactos
- Convierte expresiones simbólicas en funciones numéricas rápidas (lambdify) o en código C/Fortran/LaTeX
- Cubre cálculos de física (mecánica clásica y cuántica), teoría de números y geometría

## Cuándo usarla

- Necesitas resultados matemáticos exactos, no aproximaciones (p. ej. sqrt(2) en vez de 1.414...)
- Resolver ecuaciones, derivar, integrar o simplificar expresiones simbólicamente
- Trabajar con matrices simbólicas o hacer cálculos de física, geometría o teoría de números
- Generar código ejecutable o salida LaTeX a partir de expresiones matemáticas

## Cuándo no

- Basta con aproximaciones numéricas de punto flotante (usa NumPy o SciPy)

## Qué la activa

- "Resuelve esta ecuación simbólicamente con SymPy"
- "Calcula la derivada e integral de esta expresión"
- "Convierte esta expresión simbólica en una función de NumPy con lambdify"
- "Dame los autovalores exactos de esta matriz"

## Antes de instalar

- Requiere Python 3.9+ y SymPy 1.14+; NumPy/SciPy/Matplotlib son opcionales para los ejemplos con lambdify, y hace falta un compilador de C/Fortran para autowrap/codegen.

## Archivos

- SKILL.md — 10 KB
- references/advanced-topics.md — 13 KB
- references/code-generation-printing.md — 14 KB
- references/core-capabilities.md — 8 KB
- references/core_capabilities.md — 5 KB
- references/matrices-linear-algebra.md — 9 KB
- references/physics-mechanics.md — 12 KB

## SKILL.md

Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo https://github.com/sympy/sympy/blob/master/LICENSE. Esta sección es el documento original y está en inglés.

# SymPy - Symbolic Mathematics in Python

## Overview

SymPy is a Python library for symbolic mathematics that enables exact computation using mathematical symbols rather than numerical approximations. This skill provides comprehensive guidance for performing symbolic algebra, calculus, linear algebra, equation solving, physics calculations, and code generation using SymPy.

## Installation

Tested against **SymPy 1.14.0** (stable; April 2025). Requires **Python 3.9+**.

```bash
# Install SymPy using uv
uv pip install "sympy>=1.14"

# Optional: for lambdify and plotting examples
uv pip install numpy scipy matplotlib
```

Check your version:

```python
import sympy
print(sympy.__version__)
```

## When to Use This Skill

Use this skill when:
- Solving equations symbolically (algebraic, differential, systems of equations)
- Performing calculus operations (derivatives, integrals, limits, series)
- Manipulating and simplifying algebraic expressions
- Working with matrices and linear algebra symbolically
- Doing physics calculations (mechanics, quantum mechanics, vector analysis)
- Number theory computations (primes, factorization, modular arithmetic)
- Geometric calculations (2D/3D geometry, analytic geometry)
- Converting mathematical expressions to executable code (Python, C, Fortran)
- Generating LaTeX or other formatted mathematical output
- Needing exact mathematical results (e.g., `sqrt(2)` not `1.414...`)

## Core Capabilities

Seven capability areas are documented in
[references/core_capabilities.md](references/core_capabilities.md):

1. **Symbolic computation basics** — symbols, expressions, simplification, substitution.
2. **Calculus** — differentiation, integration, limits, series.
3. **Equation solving** — `solve`, `solveset`, linear and nonlinear systems, ODEs.
4. **Matrices and linear algebra** — see
   [references/matrices-linear-algebra.md](references/matrices-linear-algebra.md).
5. **Physics and mechanics** — see
   [references/physics-mechanics.md](references/physics-mechanics.md).
6. **Advanced mathematics** — see
   [references/advanced-topics.md](references/advanced-topics.md).
7. **Code generation and output** — see
   [references/code-generation-printing.md](references/code-generation-printing.md).

Deeper treatment of the first three is in
[references/core-capabilities.md](references/core-capabilities.md).

## Working with SymPy: Best Practices

### 1. Always Define Symbols First

```python
from sympy import symbols
x, y, z = symbols('x y z')
# Now x, y, z can be used in expressions
```

### 2. Use Assumptions for Better Simplification

```python
x = symbols('x', positive=True, real=True)
sqrt(x**2)  # Returns x (not Abs(x)) due to positive assumption
```

Common assumptions: `real`, `positive`, `negative`, `integer`, `rational`, `complex`, `even`, `odd`

### 3. Use Exact Arithmetic

```python
from sympy import Rational, S
# Correct (exact):
expr = Rational(1, 2) * x
expr = S(1)/2 * x

# Incorrect (floating-point):
expr = 0.5 * x  # Creates approximate value
```

### 4. Numerical Evaluation When Needed

```python
from sympy import pi, sqrt
result = sqrt(8) + pi
result.evalf()    # 5.96371554103586
result.evalf(50)  # 50 digits of precision
```

### 5. Convert to NumPy for Performance

```python
# Slow for many evaluations:
for x_val in range(1000):
    result = expr.subs(x, x_val).evalf()

# Fast:
f = lambdify(x, expr, 'numpy')
results = f(np.arange(1000))
```

### 6. Use Appropriate Solvers

- `solveset`: Algebraic equations (primary)
- `linsolve`: Linear systems
- `nonlinsolve`: Nonlinear systems
- `dsolve`: Differential equations
- `solve`: General purpose (legacy, but flexible)

## Reference Files Structure

This skill uses modular reference files for different capabilities:

1. **`core-capabilities.md`**: Symbols, algebra, calculus, simplification, equation solving
   - Load when: Basic symbolic computation, calculus, or solving equations

2. **`matrices-linear-algebra.md`**: Matrix operations, eigenvalues, linear systems
   - Load when: Working with matrices or linear algebra problems

3. **`physics-mechanics.md`**: Classical mechanics, quantum mechanics, vectors, units
   - Load when: Physics calculations or mechanics problems

4. **`advanced-topics.md`**: Geometry, number theory, combinatorics, logic, statistics
   - Load when: Advanced mathematical topics beyond basic algebra and calculus

5. **`code-generation-printing.md`**: Lambdify, codegen, LaTeX output, printing
   - Load when: Converting expressions to code or generating formatted output

## Common Use Case Patterns

### Pattern 1: Solve and Verify

```python
from sympy import symbols, solve, simplify
x = symbols('x')

# Solve equation
equation = x**2 - 5*x + 6
solutions = solve(equation, x)  # [2, 3]

# Verify solutions
for sol in solutions:
    result = simplify(equation.subs(x, sol))
    assert result == 0
```

### Pattern 2: Symbolic to Numeric Pipeline

```python
# 1. Define symbolic problem
x, y = symbols('x y')
expr = sin(x) + cos(y)

# 2. Manipulate symbolically
simplified = simplify(expr)
derivative = diff(simplified, x)

# 3. Convert to numerical function
f = lambdify((x, y), derivative, 'numpy')

# 4. Evaluate numerically
results = f(x_data, y_data)
```

### Pattern 3: Document Mathematical Results

```python
# Compute result symbolically
integral_expr = Integral(x**2, (x, 0, 1))
result = integral_expr.doit()

# Generate documentation
print(f"LaTeX: {latex(integral_expr)} = {latex(result)}")
print(f"Pretty: {pretty(integral_expr)} = {pretty(result)}")
print(f"Numerical: {result.evalf()}")
```

## Integration with Scientific Workflows

### With NumPy

```python
import numpy as np
from sympy import symbols, lambdify

x = symbols('x')
expr = x**2 + 2*x + 1

f = lambdify(x, expr, 'numpy')
x_array = np.linspace(-5, 5, 100)
y_array = f(x_array)
```

### With Matplotlib

```python
import matplotlib.pyplot as plt
import numpy as np
from sympy import symbols, lambdify, sin

x = symbols('x')
expr = sin(x) / x

f = lambdify(x, expr, 'numpy')
x_vals = np.linspace(-10, 10, 1000)
y_vals = f(x_vals)

plt.plot(x_vals, y_vals)
plt.show()
```

### With SciPy

```python
from scipy.optimize import fsolve
from sympy import symbols, lambdify

# Define equation symbolically
x = symbols('x')
equation = x**3 - 2*x - 5

# Convert to numerical function
f = lambdify(x, equation, 'numpy')

# Solve numerically with initial guess
solution = fsolve(f, 2)
```

## Quick Reference: Most Common Functions

```python
# Symbols
from sympy import symbols, Symbol
x, y = symbols('x y')

# Basic operations
from sympy import simplify, expand, factor, collect, cancel
from sympy import sqrt, exp, log, sin, cos, tan, pi, E, I, oo

# Calculus
from sympy import diff, integrate, limit, series, Derivative, Integral

# Solving
from sympy import solve, solveset, linsolve, nonlinsolve, dsolve

# Matrices
from sympy import Matrix, eye, zeros, ones, diag

# Logic and sets
from sympy import And, Or, Not, Implies, FiniteSet, Interval, Union

# Output
from sympy import latex, pprint, lambdify, init_printing

# Utilities
from sympy import evalf, N, nsimplify
```

## Getting Started Examples

### Example 1: Solve Quadratic Equation
```python
from sympy import symbols, solve, sqrt
x = symbols('x')
solution = solve(x**2 - 5*x + 6, x)
# [2, 3]
```

### Example 2: Calculate Derivative
```python
from sympy import symbols, diff, sin
x = symbols('x')
f = sin(x**2)
df_dx = diff(f, x)
# 2*x*cos(x**2)
```

### Example 3: Evaluate Integral
```python
from sympy import symbols, integrate, exp
x = symbols('x')
integral = integrate(x * exp(-x**2), (x, 0, oo))
# 1/2
```

### Example 4: Matrix Eigenvalues
```python
from sympy import Matrix
M = Matrix([[1, 2], [2, 1]])
eigenvals = M.eigenvals()
# {3: 1, -1: 1}
```

### Example 5: Generate Python Function
```python
from sympy import symbols, lambdify
import numpy as np
x = symbols('x')
expr = x**2 + 2*x + 1
f = lambdify(x, expr, 'numpy')
f(np.array([1, 2, 3]))
# array([ 4,  9, 16])
```

## Troubleshooting Common Issues

1. **"NameError: name 'x' is not defined"**
   - Solution: Always define symbols using `symbols()` before use

2. **Unexpected numerical results**
   - Issue: Using floating-point numbers like `0.5` instead of `Rational(1, 2)`
   - Solution: Use `Rational()` or `S()` for exact arithmetic

3. **Slow performance in loops**
   - Issue: Using `subs()` and `evalf()` repeatedly
   - Solution: Use `lambdify()` to create a fast numerical function

4. **"Can't solve this equation"**
   - Try different solvers: `solve`, `solveset`, `nsolve` (numerical)
   - Check if the equation is solvable algebraically
   - Use numerical methods if no closed-form solution exists

5. **Simplification not working as expected**
   - Try different simplification functions: `simplify`, `factor`, `expand`, `trigsimp`
   - Add assumptions to symbols (e.g., `positive=True`)
   - Use `simplify(expr, force=True)` for aggressive simplification

## Additional Resources

- Official Documentation: https://docs.sympy.org/
- Tutorial: https://docs.sympy.org/latest/tutorials/intro-tutorial/index.html
- API Reference: https://docs.sympy.org/latest/reference/index.html
- Examples: https://github.com/sympy/sympy/tree/master/examples

## Dónde encaja

- Categoría: [Herramientas para desarrolladores](https://skillsagentes.com/categorias/herramientas-desarrollo.md) — Skills que cambian cómo tu agente escribe, revisa y despliega código.
- Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

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