sympy – Coding skill for AI agents

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

Reviewed against current official documentation and executed with SymPy 1.14.0

on Python 3.13.3 (2026-10-01). Core SymPy requires Python 3.9+; the tested

NumPy 2.5.3 / SciPy 1.18.1 stack needs Python 3.12+. SymPy 1.14.0 requires

mpmath>=1.1,<1.4; use the compatible 1.3.0, not the newer 1.4.x release.

See [verification and official sources](references/review.md) for coverage.

# Install SymPy using uv
uv pip install "sympy==1.14.0"

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

Check your version:

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

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

from sympy import symbols, sqrt
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

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

# Approximate (appropriate for measured/numerical inputs):
expr = 0.5 * x  # Creates approximate value

4. Numerical Evaluation When Needed

from sympy import pi, sqrt
result = sqrt(8) + pi
result.evalf()    # 5.96371554103586
result.evalf(50)  # Request 50 decimal digits; cannot recover precision lost in inputs

5. Convert to NumPy for Performance

from sympy import symbols, lambdify
import numpy as np
x = symbols("x")
expr = x**2 + 1
# 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; supports some problems solveset does not

Declare the solution domain: solveset defaults to complex numbers, so use domain=S.Reals for real-only questions. A returned ConditionSet means an unresolved solution condition, not that no solutions exist; distinguish it from EmptySet. A numerical nsolve result is a local root found from a starting point, not proof that every root was found.

7. Preserve mathematical meaning and input trust

Use assumptions only when justified by the problem. An unconstrained symbol is

complex; sqrt(x**2) need not equal x, and logarithm/power identities depend on

branches. Assumption predicates can return None (unknown). Keep excluded

denominator zeros when cancelling factors, and verify candidate solutions in the

original expression and requested domain. == compares symbolic structure; use

Eq to build an equation and targeted simplification to verify an identity.

parse_expr, string sympify, and lambdify can execute code. Accept only trusted

expressions there. A regex, local_dict, or evaluate=False is not a security

boundary; untrusted input needs a separate allowlisted grammar that constructs

SymPy objects, plus resource limits. See the code-generation reference.

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

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

from sympy import symbols, sin, cos, simplify, diff, lambdify
import numpy as np
x_data = np.linspace(0, 1, 5)
y_data = np.linspace(1, 2, 5)
# 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

from sympy import symbols, Integral, latex, pretty
x = symbols("x")
# 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

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

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

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, info, status, message = fsolve(f, 2, full_output=True)
assert status == 1, message
assert abs(f(solution[0])) < 1e-10
# A converged local root is not a complete root set.

Quick Reference: Most Common Functions

# 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

… (full content is in the download package)