# SymPy

last modified July 6, 2020

SymPy tutorial shows how to do symbolic computation in Python with sympy module. This is a brief introduction to the SymPy.

Computer algebra system (CAS) is a mathematical software with the ability to manipulate mathematical expressions in a way similar to the traditional manual computations of mathematicians and scientists.

*Symbolic computation* deals with the computation of mathematical objects
symbolically. The mathematical objects are represented exactly, not
approximately, and mathematical expressions with unevaluated variables are left
in symbolic form.

## SymPy

SymPy is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system. SymPy includes features ranging from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. It is capable of showing results in LaTeX.

$ pip install sympy

SymPy is installed with `pip install sympy`

command.

## Rational values

SymPy has `Rational`

for working with rational numbers. A rational
number is any number that can be expressed as the quotient or fraction p/q of
two integers, a numerator p and a non-zero denominator q.

#!/usr/bin/env python from sympy import Rational r1 = Rational(1/10) r2 = Rational(1/10) r3 = Rational(1/10) val = (r1 + r2 + r3) * 3 print(val.evalf()) val2 = (1/10 + 1/10 + 1/10) * 3 print(val2)

The example works with rational numbers.

val = (r1 + r2 + r3) * 3 print(val.evalf())

The expression is in the symbolic form; we evaluate it with
`evalf()`

method.

$ rational_values.py 0.900000000000000 0.9000000000000001

Notice that there is a small error in the output when not using rational numbers.

## SymPy pprint

The `pprint()`

is used for pretty printing the output on the console.
The best results are achieved with LaTeX e.g. in Jupyter notebook.

#!/usr/bin/env python from sympy import pprint, Symbol, exp, sqrt from sympy import init_printing init_printing(use_unicode=True) x = Symbol('x') a = sqrt(2) pprint(a) print(a) print("------------------------") c = (exp(x) ** 2)/2 pprint(c) print(c)

The program prettifies the output.

init_printing(use_unicode=True)

For some characters we need to enable unicode support.

$ prettify.py √2 sqrt(2) ------------------------ 2⋅x ℯ ──── 2 exp(2*x)/2

This is the output. Note that using Jupyter notebook gives much nicer output.

## Square root

Square root is a number which produces a specified quantity when multiplied by itself.

#!/usr/bin/env python from sympy import sqrt, pprint, Mul x = sqrt(2) y = sqrt(2) pprint(Mul(x, y, evaluate=False)) print('equals to ') print(x * y)

The program outputs an expression containing square roots.

pprint(Mul(x, y, evaluate=False))

We postpone the evaluation of the multiplication expression
with `evaluate`

attribute.

$ square_root.py √2⋅√2 equals to 2

This is the output.

## SymPy symbols

Symbolic computation works with symbols, which may be later evaluated. Symbols must be defined in SymPy before using them.

#!/usr/bin/env python # ways to define symbols from sympy import Symbol, symbols from sympy.abc import x, y expr = 2*x + 5*y print(expr) a = Symbol('a') b = Symbol('b') expr2 = a*b + a - b print(expr2) i, j = symbols('i j') expr3 = 2*i*j + i*j print(expr3)

The programs shows three ways to define symbols in SymPy.

from sympy.abc import x, y

Symbols can be imported from the `sympy.abc`

module.
It exports all latin and greek letters as Symbols, so we can
conveniently use them.

a = Symbol('a') b = Symbol('b')

They can be defined with `Symbol`

i, j = symbols('i j')

Multiple symbols can be defined with `symbols()`

method.

## SymPy canonical form of expression

An expression is automatically transformed into a canonical form by SymPy. SymPy does only inexpensive operations; thus the expression may not be evaluated into its simplest form.

#!/usr/bin/env python from sympy.abc import a, b expr = b*a + -4*a + b + a*b + 4*a + (a + b)*3 print(expr)

We have an expression with symbols `a`

and `b`

.
The expression can be easily simplified.

$ canonical_form.py 2*a*b + 3*a + 4*b

This is the output.

## SymPy expanding algebraic expressions

With `expand()`

, we can expand algebraic expressions;
i.e. the method tries to denest powers and multiplications.

#!/usr/bin/env python from sympy import expand, pprint from sympy.abc import x expr = (x + 1) ** 2 pprint(expr) print('-----------------------') print('-----------------------') expr = expand(expr) pprint(expr)

The program expands a simple expression.

$ expand.py 2 (x + 1) ----------------------- ----------------------- 2 x + 2⋅x + 1

This is the output.

## SymPy simplify an expression

An expression can be changed with `simplify()`

to a
simpler form.

#!/usr/bin/env python from sympy import sin, cos, simplify, pprint from sympy.abc import x expr = sin(x) / cos(x) pprint(expr) print('-----------------------') expr = simplify(expr) pprint(expr)

The exaple simflifies a `sin(x)/sin(y)`

expression to `tan(x)`

.

$ simplify.py sin(x) ────── cos(x) ----------------------- tan(x)

This is the output.

## SymPy comparig expression

SymPy expressions are compared with `equals()`

and
not with `==`

operator.

#!/usr/bin/env python from sympy import pprint, Symbol, sin, cos x = Symbol('x') a = cos(x)**2 - sin(x)**2 b = cos(2*x) print(a.equals(b)) # we cannot use == operator print(a == b)

The program compares two expressions.

print(a.equals(b))

We compare two expressions with `equals()`

. Before applying
the method, SymPy tries to simplify the expressions.

$ expr_equality.py True False

This is the output.

## SymPy evaluating expression

Expressions can be evaluated by substitution of symbols.

#!/usr/bin/env python from sympy import pi print(pi.evalf(30))

The example evaluates a pi value to thirty places.

$ evaluating.py 3.14159265358979323846264338328

This is the output.

#!/usr/bin/env python from sympy.abc import a, b from sympy import pprint expr = b*a + -4*a + b + a*b + 4*a + (a + b)*3 print(expr.subs([(a, 3), (b, 2)]))

The example evaluates an expression by substituting `a`

and `b`

symbols with numbers.

$ evaluating.py 3.14159265358979323846264338328

This is the output.

## SymPy solving equations

Equations are solved with `solve()`

or `solveset()`

.

#!/usr/bin/env python from sympy import Symbol, solve x = Symbol('x') sol = solve(x**2 - x, x) print(sol)

The example solves a simple equation with `solve()`

.

sol = solve(x**2 - x, x)

The first parameter of the `solve()`

is the equation.
The equation is written in a specific form, suitable for SymPy; i.e.
`x**2 - x`

instead of `x**2 = x`

. The second
paramter is the symbol for which we need solution.

$ solving.py [0, 1]

The equation has two solutions: 0 and 1.

Alternatively, we can use the `Eq`

for equation.

#!/usr/bin/env python from sympy import pprint, Symbol, Eq, solve x = Symbol('x') eq1 = Eq(x + 1, 4) pprint(eq1) sol = solve(eq1, x) print(sol)

The example solves a simple `x + 1 = 4`

equation.

$ solving2.py x + 1 = 4 [3]

This is the output.

#!/usr/bin/env python from sympy.solvers import solveset from sympy import Symbol, Interval, pprint x = Symbol('x') sol = solveset(x**2 - 1, x, Interval(0, 100)) print(sol)

With `solveset()`

, we find a solution for the
given interval.

$ solving3.py {1}

This is the output.

## SymPy sequence

Sequence is an enumerated collection of objects in which repetitions are allowed. A sequence can be finite or infinite. The number of elements is called the length of the sequence. Unlike a set, the same elements can appear multiple times at different positions in a sequence. The order of elements matters.

#!/usr/bin/env python from sympy import summation, sequence, pprint from sympy.abc import x s = sequence(x, (x, 1, 10)) print(s) pprint(s) print(list(s)) print(s.length) print(summation(s.formula, (x, s.start, s.stop))) # print(sum(list(s)))

The example creates a sequence of numbers 1, 2,...,10. We compute the sum of these numbers.

$ sequence.py SeqFormula(x, (x, 1, 10)) [1, 2, 3, 4, …] [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 10 55

This is the output.

## SymPy limit

A limit is the value that a function (or sequence) "approaches" as the input (or index) "approaches" some value.

#!/usr/bin/env python from sympy import sin, limit, oo from sympy.abc import x l1 = limit(1/x, x, oo) print(l1) l2 = limit(1/x, x, 0) print(l2)

In the example, we have the `1/x`

function. It has a left-sided and
right-sided limit.

from sympy import sin, limit, sqrt, oo

The `oo`

denotes infinity.

l1 = limit(1/x, x, oo) print(l1)

We calculate the limit of `1/x`

where x approaches positive infinity.

$ limit.py 0 oo

This is the output.

## SymPy matrixes

In SymPy, we can work with matrixes. A matrix is a rectangular array of numbers or other mathematical objects for which operations such as addition and multiplication are defined.

Matrixes are used in computing, engineering, or image processing.

#!/usr/bin/env python from sympy import Matrix, pprint M = Matrix([[1, 2], [3, 4], [0, 3]]) print(M) pprint(M) N = Matrix([2, 2]) print("---------------------------") print("M * N") print("---------------------------") pprint(M*N)

The example defines two matrixes and multiplies them.

$ matrix.py Matrix([[1, 2], [3, 4], [0, 3]]) ⎡1 2⎤ ⎢ ⎥ ⎢3 4⎥ ⎢ ⎥ ⎣0 3⎦ --------------------------- M * N --------------------------- ⎡6 ⎤ ⎢ ⎥ ⎢14⎥ ⎢ ⎥ ⎣6 ⎦

This is the output.

## SymPy plotting

SymPy contains module for plotting. It is built on Matplotlib.

#!/usr/bin/env python # uses matplotlib import sympy from sympy.abc import x from sympy.plotting import plot plot(1/x)

The example plots a 2d graph of a `1/x`

function.

This was SymPy tutorial.

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