Sympy - Symbolic algebra in Python
J.R. Johansson (jrjohansson at gmail.com)
The latest version of this IPython notebook lecture is available at http://github.com/jrjohansson/scientific-python-lectures.
The other notebooks in this lecture series are indexed at http://jrjohansson.github.io.
There are two notable Computer Algebra Systems (CAS) for Python:
SymPy - A python module that can be used in any Python program, or in an IPython session, that provides powerful CAS features.
Sage - Sage is a full-featured and very powerful CAS enviroment that aims to provide an open source system that competes with Mathematica and Maple. Sage is not a regular Python module, but rather a CAS environment that uses Python as its programming language.
Sage is in some aspects more powerful than SymPy, but both offer very comprehensive CAS functionality. The advantage of SymPy is that it is a regular Python module and integrates well with the IPython notebook.
In this lecture we will therefore look at how to use SymPy with IPython notebooks. If you are interested in an open source CAS environment I also recommend to read more about Sage.
To get started using SymPy in a Python program or notebook, import the module
To get nice-looking formatted output run:
In SymPy we need to create symbols for the variables we want to work with. We can create a new symbol using the
We can add assumptions to symbols when we create them:
The imaginary unit is denoted
I in Sympy.
There are three different numerical types in SymPy:
SymPy uses a library for artitrary precision as numerical backend, and has predefined SymPy expressions for a number of mathematical constants, such as:
oo for infinity.
To evaluate an expression numerically we can use the
evalf function (or
N). It takes an argument
n which specifies the number of significant digits.
When we numerically evaluate algebraic expressions we often want to substitute a symbol with a numerical value. In SymPy we do that using the
subs function can of course also be used to substitute Symbols and expressions:
We can also combine numerical evolution of expressions with NumPy arrays:
However, this kind of numerical evolution can be very slow, and there is a much more efficient way to do it: Use the function
lambdify to "compile" a Sympy expression into a function that is much more efficient to evaluate numerically:
The speedup when using "lambdified" functions instead of direct numerical evaluation can be significant, often several orders of magnitude. Even in this simple example we get a significant speed up:
One of the main uses of an CAS is to perform algebraic manipulations of expressions. For example, we might want to expand a product, factor an expression, or simply an expression. The functions for doing these basic operations in SymPy are demonstrated in this section.
Expand and factor
The first steps in an algebraic manipulation
expand function takes a number of keywords arguments which we can tell the functions what kind of expansions we want to have performed. For example, to expand trigonometric expressions, use the
trig=True keyword argument:
help(expand) for a detailed explanation of the various types of expansions the
expand functions can perform.
The opposite a product expansion is of course factoring. The factor an expression in SymPy use the
simplify tries to simplify an expression into a nice looking expression, using various techniques. More specific alternatives to the
simplify functions also exists:
The basic usages of these functions are as follows:
apart and together
To manipulate symbolic expressions of fractions, we can use the
Simplify usually combines fractions but does not factor:
In addition to algebraic manipulations, the other main use of CAS is to do calculus, like derivatives and integrals of algebraic expressions.
Differentiation is usually simple. Use the
diff function. The first argument is the expression to take the derivative of, and the second argument is the symbol by which to take the derivative:
For higher order derivatives we can do:
To calculate the derivative of a multivariate expression, we can do:
Integration is done in a similar fashion:
By providing limits for the integration variable we can evaluate definite integrals:
and also improper integrals
oo is the SymPy notation for inifinity.
Sums and products
We can evaluate sums and products using the functions: 'Sum'
Products work much the same way:
Limits can be evaluated using the
limit function. For example,
We can use 'limit' to check the result of derivation using the
We can change the direction from which we approach the limiting point using the
dir keywork argument:
Series expansion is also one of the most useful features of a CAS. In SymPy we can perform a series expansion of an expression using the
By default it expands the expression around , but we can expand around any value of by explicitly include a value in the function call:
And we can explicitly define to which order the series expansion should be carried out:
The series expansion includes the order of the approximation, which is very useful for keeping track of the order of validity when we do calculations with series expansions of different order:
If we want to get rid of the order information we can use the
But note that this is not the correct expansion of to th order:
Matrices are defined using the
Matrix class instances we can do the usual matrix algebra operations:
And calculate determinants and inverses, and the like:
For solving equations and systems of equations we can use the
System of equations:
In terms of other symbolic expressions:
http://sympy.org/en/index.html - The SymPy projects web page.
https://github.com/sympy/sympy - The source code of SymPy.
http://live.sympy.org - Online version of SymPy for testing and demonstrations.