# Glossary#

This page is a glossary for various terms used throughout the SymPy documentation. This glossary is primarily for terms that are specific to SymPy. For more general Python terms, refer to the Python glossary. Mathematical terms are only included here if they have a specific meaning in SymPy. For general mathematical definitions, refer to other sources such as Wikipedia or MathWorld, as well as the references in the documentation for the specific SymPy functions.

Antiderivative#

An antiderivative of a function $$f(x)$$ with respect to $$x$$ is a function $$F(x)$$ such that $$\frac{d}{dx}F(x) = f(x).$$ It is also sometimes called an “indefinite integral” of $$f(x)$$, and written as $$\int f(x)\,dx.$$ Antiderivatives in SymPy can be computed with integrate(). Note some sources call this the “primitive” of $$f(x)$$, but this terminology is not used in SymPy because it is not as universally used as “antiderivative”, and because “primitive” has other meanings in mathematics and in SymPy.

args#

The args property of a SymPy expression is a tuple of the top-level subexpressions used to create it. They are the arguments to the class used to create the expression. The args of any expression can be obtained by the .args attribute. For example, (1 + x*y).args is (1, x*y), because it equals Add(1, x*y). The args together with func completely define an expression. It is always possible to walk the expression tree and extract any subexpression of a SymPy expression by repeated use of .args. Every SymPy expression can be rebuilt exactly with func and args, that is, expr.func(*expr.args) == expr will always be true of any SymPy expression expr. The args of an expression may be the empty tuple (), meaning the expression is an atom.

Assumptions#

Assumptions are a set of predicates on a symbol or expression that define the set of possible values it can take. Some examples of assumptions are positive, real, and integer. Assumptions are related to one another logically, for example, an assumption of integer automatically implies real. Assumptions use a three-valued logic system where predicates are either True, False, or None.

Assumptions are either assumed or queried. For example, a symbol x might be assumed to be positive by defining it as x = symbols('x', positive=True). Then an assumption might be queried on the expression containing this symbol, like (x + 1).is_real, which in this case would return True.

If no assumptions are assumed on a symbol, then by default symbols are assumed to be general complex numbers. Setting assumptions is important because certain simplifications are only mathematically true in a restricted domain, for example, $$\sqrt{x^2} = x$$ is not true for general complex $$x$$ but it is true when $$x$$ is positive. SymPy functions will never perform an operation on an expression unless it is true for all values allowed by its assumptions.

SymPy has two separate assumptions systems, which are closely related to one another. In the first, which is sometimes called the “old assumptions” because it is older, assumptions are assumed on Symbol objects and queried with is_* attributes. In the second, which is sometimes called the “new assumptions”, assumptions are assumed using separate predicate objects like Q.positive and queried using the ask() function. The newer assumptions system is able to support more complex queries, but is also not as well developed as the older one. Most users of SymPy should prefer the older assumptions system at this time.

See the assumptions guide for more details on assumptions.

Atom#

An atom is an expression whose args is the empty tuple (). Atoms are the leaves of the expression tree. For example, if a function uses recursion to walk an expression tree using args, the atomic expressions will be the base case of the recursion.

Note that the class Atom is sometimes used as the base class of atomic expressions, but it is not a requirement for atomic expressions to subclass this class. The only requirement for an expression to be atomic is for its args to be empty.

Automatic Simplification#

Automatic Simplification refers to any simplification that happens automatically inside of a class constructor. For example, x + x is automatically simplified to 2*x in the Add constructor. Unlike manual simplification, automatic simplification can only be disabled by setting evaluate=False (see Unevaluated). Automatic simplification is often done so that expressions become canonicalized. Excessive automatic simplification is discouraged, as it makes it impossible to represent the non-simplified form of the expression without using tricks like evaluate=False, and it can often be an expensive thing to do in a class constructor. Instead, manual simplification/canonicalization is generally preferred.

Basic#

Basic is the superclass of all SymPy expressions. It defines the basic methods required for a SymPy expression, such as args, func, equality, immutability, and some useful expression manipulation functions such as substitution. Most SymPy classes will subclass a more specific Basic subclass such as Boolean, Expr, Function, or Matrix. An object that is not a Basic instance typically cannot be used in SymPy functions, unless it can be turned into one via sympify().

Boolean#

Boolean is the base class for the classes in the logic module. Boolean instances represent logical predicates that are elements of a boolean algebra and can be thought of as having a “true” or “false” value (note that Boolean objects do not use the three-valued logic used by the assumptions).

Bound symbols#

A symbol in an expression is bound if it is not free. A bound symbol can be replaced everywhere with new symbol and the resulting expression will still be mathematically equivalent. Examples of bound symbols are integration variables in definite integrals and substituted variables in a Subs. Bound symbols are sometimes represented by dummy symbols, but the are not always Dummy objects, and Dummy objects are not always bound symbols.

Canonical Form#
Canonicalize#

Often expressions can be written in multiple, mathematically equivalent ways. A canonical form is a single way of writing an expression, which all equivalent expressions can be transformed to. An expression that is put into a canonical form is said to be canonicalized. Often canonical forms are unique and have properties that make them easier to work with. For example, a common canonical form used for rational functions is $$\frac{p}{q}$$, where $$p$$ and $$q$$ are expanded polynomials with no common factors.

Code Generation#

Code generation refers to the process of taking a SymPy expression and converting it into code for a language or library so that it can be evaluated numerically. SymPy supports code generation for dozens of languages and libraries including C, C++, Fortran, and NumPy.

Core#

The core is the submodule that contains the important functionality used by all SymPy objects. This includes the Basic and Expr base classes, classes like Add, Mul, and Pow, and the assumptions.

Dummy#

A dummy symbol is a symbol that is automatically unequal to any other dummy symbol other than itself, even if it has the same name. Dummy symbols are used when a function needs to return an expression with a new symbol, so that it cannot accidentally clash with a symbol of the same name. Dummy symbols can be created with Dummy.

Equation#

An equation is an expression that has an equals sign $$=$$. Equations in SymPy are represented using the Eq class. Equations are not created using the == operator. The == operator does a structural equality check between two expressions, and always returns True or False. To contrast, a symbolic equation may be unevaluated. Equations are considered booleans since they mathematically represent a predicate value that is either true or false.

_eval_*#

Various methods on Basic and Expr can be defined on subclasses using special _eval_* methods. For example, an object can define how it will be processed by the diff() function by defining a _eval_derivative method. _eval_* methods used are instead of overriding the method itself so that the method defined on the base class can do pre-processing before dispatching to the _eval_* method.

evalf#

evalf is the method present on every Expr object that evaluates it to a floating-point numerical value, or converts the constant parts of the expression to a numerical value if it contains symbols. The .n() method and N() function are both shorthands for evalf. “evalf” stands for “evaluate floating-point”. evalf uses mpmath under the hood to evaluate expressions to arbitrary precision.

Evaluate#

Evaluate can refer to:

Expr#

Expr is the superclass of all algebraic SymPy expressions. It is itself a subclass of Basic. SymPy expressions that can be in an Add, Mul, or Pow should be Expr subclasses. Not all SymPy classes are subclasses of Expr, for example, Boolean objects are Basic but not Expr, because boolean expressions do not make mathematical sense in classes like Add or Mul.

Expression#

Any SymPy object, that is, any instance of Basic, may be called an expression. Sometimes, the term “expression” is reserved for Expr objects, which are algebraic expressions. Expressions are not to be confused with equations, which are a specific types of expressions that represents mathematical equalities.

Expression Tree#

An expression tree is a tree of expressions. Every expression is built up from smaller expressions as a tree. The nodes of an expression tree are expressions and the children of each node are the direct subexpressions that constitute that expression. Alternatively, one can view an expression tree as a tree where the non-leaf nodes are funcs and the leaf nodes are atoms. An example expression tree is shown in the tutorial. The expression tree of any SymPy expression can be obtained by recursing through args. Note that because SymPy expressions are immutable and are treated equal strictly by structural equality, one may also think of an expression tree as being a DAG, where identical subexpressions are only represented in the graph once.

Free symbols#

A symbol in an expression is free if the expression mathematically depends on the value of that symbol. That is, if the symbol were replaced with a new symbol, the result would be a different expression. Symbols that are not free are bound. The free symbols of an expression can be accessed with the free_symbols attribute.

func#

The func property is the function of an expression, which can be obtained by expr.func. This is usually the same as type(expr), but may differ in some cases, so it should be preferred to use expr.func instead of type(expr) when rebuilding expressions with args. Every SymPy expression can be rebuilt exactly with func and args, that is, expr.func(*expr.args) == expr will always be true of any SymPy expression expr.

Function#

Function may refer to:

• A mathematical function, that is, something which maps values from a domain to a range. Sometimes an expression containing a symbol is colloquially called a “function” because the symbol can be replaced with a value using substitution, evaluating the expression. This usage is colloquial because one must use the subs method to do this rather than the typical Python function calling syntax, and because it is not specific about what variable(s) the expression is a function of, so generally the term “expression” should be preferred unless something is an actual function. An expression can be converted into a function object that can be called using the Python f(x) syntax using Lambda.

• An instance of the SymPy Function class.

• A Python function, i.e., a function defined using the def keyword. Python functions are not symbolic, since they must always return a value and thus cannot be unevaluated.

Function (class)#

Function is the base class of symbolic functions in SymPy. This includes common functions like sin() and exp(), special functions like zeta() and hyper(), and integral functions like primepi() and divisor_sigma(). Function classes are always symbolic, meaning that they typically remain unevaluated when passed a symbol, like f(x). Not every symbolic expression class is a Function subclass, for example, core classes like Add and Mul are not Function subclasses.

Function may also be used to create an undefined function by passing it a string name for the function, like Function('f').

Not every function in SymPy is a symbolic Function class; some are just Python functions which always return a value. For example, most simplification functions like simplify() cannot be represented symbolically.

Immutable#

In Python, objects are immutable if they can not be modified in-place. In order to change an immutable object, a new object must be created. In SymPy, all Basic objects are immutable. This means that all functions that operate on expressions will return a new expression and leave the original unchanged. Performing an operation on an expression will never change other objects or expressions that reference that expression. This also means that any two objects that are equal are completely interchangeable and may be thought of as being the same object, even if they happen to be two different objects in memory. Immutability makes it easier to maintain a mental model of code, because there is no hidden state. SymPy objects being immutable also means that they are hashable, which allows them to be used as dictionary keys.

Interactive#

Interactive usage refers to using SymPy in an interactive REPL environment such as the Python prompt, isympy, IPython, or the Jupyter notebook. When using SymPy interactively, all commands are typed in real time by the user and all intermediate results are shown. Interactive use is in contrast with programmatic use, which is where the code is written in a file which is either executed as a script or is part of a larger Python library. Some SymPy idioms are only recommended for interactive use and are considered anti-patterns when used programmatically. For example, running from sympy import * is convenient when using SymPy interactively, but is generally frowned upon for programmatic usage, where importing names explicitly just using import sympy is preferred.

is_*#

Attributes in SymPy that start with is_ and use a lowercase name query the given assumption on that object (note: there are a few properties that are an exception to this because they do not use the assumptions system, see the assumptions guide). For example, x.is_integer will query the integer assumption on x. is_* attributes that use a Capitalized name test if an object is an instance of the given class. Sometimes the same name will exist for both the lowercase and Capitalized property, but they are different things. For example, x.is_Integer is only True if x is an instance of Integer, whereas x.is_integer is True if x is integer in the assumptions system, such as x = symbols('x', integer=True). In general, it is recommended to not use is_Capitalized properties. They exist for historical purposes, but they are unneeded because the same thing can be achieved with isinstance(). See also Number.

isympy#

isympy is a command that ships with SymPy that starts an interactive session on the command line with all SymPy names imported and printing enabled. It uses IPython by default when it is installed.

Kind#

The kind of a SymPy object represents what sort of mathematical object it represents. The kind of an object can be accessed with the kind attribute. Example kinds are NumberKind, which represents complex numbers, MatrixKind, which represents matrices of some other kind, and BooleanKind, which represents boolean predicates. The kind of a SymPy object is distinct from its Python type, since sometimes a single Python type may represent many different kinds of objects. For example, Matrix could be a matrix of complex numbers or a matrix of objects from some other ring of values. See the classification of SymPy objects page for more details about kinds in SymPy.

lamda#

Lamda” is just an alternate spelling of the Greek letter “lambda”. It is used sometimes in SymPy because lambda is a reserved keyword in Python, so a symbol representing λ must be named something else.

lambdify()#

lambdify() is a function that converts a SymPy expression into a Python function that can be evaluated numerically, typically making use of a numeric library such as NumPy.

Matrix#

Matrix refers to the set of classes used by SymPy to represent matrices. SymPy has several internal classes to represent matrices, depending on whether the matrix is symbolic (MatrixExpr) or explicit, mutable or immutable, dense or sparse, and what type the underlying elements are, but these are often all just called “Matrix”.

mpmath#

mpmath is a pure Python library for arbitrary precision numerics. It is a hard dependency of SymPy. mpmath is capable of computing numerical functions to any given number of digits. mpmath is used under the hood whenever SymPy evaluates an expression numerically, such as when using evalf.

Numeric#

A numeric representation or algorithm is one that operates directly on numeric inputs. It is in contrast with a symbolic representation or algorithm, which can work with objects in an unevaluated form. Often a numerical algorithm is quite different from a symbolic one. For example, numerically solving an ODE typically means evaluating the ODE using an algorithm like Runge–Kutta to find a set of numeric points given an initial condition, whereas symbolically solving an ODE (such as with SymPy’s dsolve()) means mathematically manipulating the ODE to produce a symbolic equation that represents the solution. A symbolic ODE solution may including symbolic constants which can represent any numerical value. Numeric algorithms are typically designed around issues caused by floating-point numbers such as loss of precision and numerical stability, whereas symbolic algorithms are not concerned with these things because they compute things exactly.

Most scientific libraries other than SymPy, such as NumPy or SciPy, are strictly numerical, meaning the functions in those libraries can only operate on specific numeric inputs. They will not work with SymPy expressions, because their algorithms are not designed to work with symbolic inputs. SymPy focuses on symbolic functions, leaving purely numerical code to other tools like NumPy. However, SymPy does interface with numerical libraries via tools like code generation and lambdify().

Number#

Number can refer to two things in SymPy:

This distinction is important for the is_Number and is_number properties. x.is_Number will check if x is an instance of the class Number.

oo#

oo is the SymPy object representing positive infinity. It is spelled this way, as two lower case letter Os, because it resembles the symbol $$\infty$$ and is easy to type. See also zoo.

Polys#

The polys refers to the sympy.polys submodule, which implements the basic data structures and algorithms for polynomial manipulation. The polys are a key part of SymPy (though not typically considered part of the core), because many basic symbolic manipulations can be represented as manipulations on polynomials. Many algorithms in SymPy make use of the polys under the hood. For example, factor() is a wrapper around the polynomial factorization algorithms that are implemented in the polys. The classes in the polys are implemented using efficient data structures, and are not subclasses of Basic like the other classes in SymPy.

Printing#

Printing refers to the act of taking an expression and converting it into a form that can be viewed on screen. Printing is also often used to refer to code generation. SymPy has several printers which represent expressions using different formats. Some of the more common printers are the string printer (str()), the pretty printer (pprint()) the LaTeX printer (latex()), and code printers.

Relational#

A relational is an expression that is a symbolic equality (like $$a=b$$), or a symbolic inequality like “less than” ($$a<b$$). Equality ($$=$$) and non-equality ($$\neq$$) relationals are created with Eq and Ne, respectively. For example, Eq(x, 0) represents $$x=0$$. These should be used instead of == or !=, as these are used for structural rather than symbolic equality. Inequality relationals can be created directly using <, <=, >, and >=, like x < 0.

S#

The S object in SymPy has two purposes:

• It holds all singleton classes as attributes. Some special classes in SymPy are singletonized, meaning that there is always exactly one instance of them. This is an optimization that allows saving memory. For instance, there is only ever one instance of Integer(0), which is available as S.Zero.

• It serves as a shorthand for sympify(), that is S(a) is the same as sympify(a). This is useful for converting integers to SymPy Integers in expressions to avoid dividing Python ints (see the gotchas section of the tutorial).

Simplification#

Simplification (not to be confused with sympify) refers to the process of taking an expression and transforming it into another expression that is mathematically equivalent but which is somehow “simpler”. The adjective “simple” is actually not very well-defined. What counts as simpler depends on the specific use-case and personal aesthetics.

The SymPy function simplify() heuristically tries various simplification algorithms to try to find a “simpler” form of an expression. If you aren’t particular about what you want from “simplify”, it may be a good fit. But if you have an idea about what sort of simplification you want to apply, it is generally better to use one or more of targeted simplification functions which apply very specific mathematical manipulations to an expression.

Solve#
Solvers#

To solve an equation or system of equations means to find a set of expressions that make the equation(s) true when the given symbol(s) are substituted with them. For example, the solution to the equation $$x^2 = 1$$ with respect to $$x$$ would be the set $$\{-1, 1\}$$. Different types of equations can be solved by SymPy using different solvers functions. For instance, algebraic equations can be solved with solve(), differential equations can be solved with dsolve(), and so on.

SymPy generally uses the word “solve” and “solvers” to mean equation solving in this sense. It is not used in the sense of “solving a problem”. For instance, one would generally prefer to say “compute an integral” or “evaluate an integral” rather than “solve an integral” to refer to symbolic integration using the function integrate().

Structural Equality#

Two SymPy objects are structurally equal if they are equal as expressions, that is, they have the same expression trees. Two structurally equal expressions are considered to be identical by SymPy, since all SymPy expressions are immutable. Structural equality can be checked with the == operator, which always returns True or False. Symbolic equality can be represented using Eq.

Typically, two expressions are structurally equal if they are the same class and (recursively) have the same args. Two expressions may be mathematically identical but not structurally equal. For example, (x + 1)**2 and x**2 + 2*x + 1 are mathematically equal, but they are not structurally equal, because the first is a Pow whose args consist of an Add and an Integer, and the second is an Add whose args consist of a Pow, a Mul, and an Integer.

Two apparently different expressions may be structurally equal if they are canonicalized to the same thing by automatic simplification. For example, x + y and y + x are structurally equal because the Add constructor automatically sorts its arguments, making them both the same.

Subexpression#

A subexpression is an expression that is contained within a larger expression. A subexpression appears somewhere in the expression tree. For Add and Mul terms, commutative and associative laws may be taken into account when determining what is a subexpression. For instance, x + y may sometimes be considered a subexpression of x + y + z, even though the expression tree for Add(x, y) is not a direct child of the expression tree for Add(x, y, z).

Substitution#

Substitution refers to the act of replacing a symbol or subexpression inside of an expression with another expression. There are different methods in SymPy for performing substitution, including subs, replace, and xreplace. The methods may differ depending on whether they perform substitution using only strict structural equality or by making use of mathematical knowledge when determining where a subexpression appears in an expression. Substitution is the standard way to treat an expression as a mathematical function and evaluate it at a point.

Symbolic#

A symbolic representation of a mathematical object is a representation that is partially or completely unevaluated at runtime. It may include named symbolic constants in place of explicit numeric values. A symbolic representation is often contrasted with a numeric one. Symbolic representations are mathematically exact, to contrast with numeric representations which are typically rounded so they can fit within a floating-point value. Symbolic expressions representing mathematical objects may be aware of mathematical properties of these objects and be able to simplify to equivalent symbolic expressions using those properties. The goal of SymPy is to represent and manipulate symbolic expressions representing various mathematical objects.

Some sources use the phrases “analytic solution” or “closed-form” to refer to the concept of “symbolic”, but this terminology is not used in SymPy. If used in SymPy, “analytic” would refer to the property of being an analytic function, and in SymPy solve refers only to a certain type of symbolic operation. “Closed-form” in SymPy would typically refer to the mathematical sense of the term, whereas “symbolic” would generally refer to the implementation detail of how a mathematical concept is implemented, and be in contrast with a numeric implementation of the same mathematical concept.

Symbol#

Symbol is the class for symbol objects. A symbol represents a single mathematical variable in an expression. The Symbol class is a subclass of Expr and is atomic. A Symbol contains a name, which is any string, and assumptions. Symbols are typically defined with the Symbol constructor or the symbols() function. Two Symbols with the same name and assumptions are considered equal. Symbols are implicitly assumed to be independent or constant with respect to one another. Constants, variables, and parameters are all represented by Symbols. The distinction is generally made in the way the Symbols are used in a given SymPy function.

sympify()#

sympify() (not to be confused with simplify()) is a function that converts non-SymPy objects into SymPy objects. The result of sympify() will be an instance of Basic. Objects that can be sympified include native Python numeric types such as int and float, strings that are parsable as SymPy expressions, and iterables containing sympifiable objects (see the documentation for sympify() for more information).

Since all SymPy expressions must be instances of Basic, all SymPy functions and operations will implicitly call sympify() on their inputs. For example, x + 1 implicitly calls sympify(1) to convert the 1 that is a Python int into a SymPy Integer. Functions that accept SymPy expressions should typically call sympify() on their arguments so that they work even when the input is not a SymPy type.

Three-valued logic#

Three-valued logic is a logic with three values, True, False, and None. It is also sometimes called fuzzy logic, although this term also has different meanings in the mathematical literature, so “three-valued logic” is preferred. True and False work the same as in the usual two-valued predicate logic. None is an additional term that represents “unknown”, “noncomputable”, or “could be either True or False” (philosophically these are distinct concepts, but logically they all function identically). The semantics of None are that it absorbs other terms in logical operations whenever the result would differ if it were replaced with True or False. For example, None OR False is None, but None OR True is True because the predicate is True whether the None “really” represents a value of True or False. One must be careful when using the usual Python logical operators like and, or and not on three-valued logic, since None is false. See the guide for symbolic and fuzzy booleans for more details on how to code with three-valued logic.

Three-valued logic is used by the assumptions system to represent assumptions that are not known. For instance, x.is_positive might be None if x could be positive or negative under its given assumptions. Note that the predicate logic defined by Boolean subclasses represents a standard two-valued logic, not three-valued logic.

Undefined Function#

An undefined function is a Function that has no mathematical properties defined on it. It always remains unevaluated, like f(x). An undefined function can be created by passing a string name of the function to Function, like f = Function('f'). Undefined functions are commonly used when working with ODEs. Undefined functions are also the easiest way to make symbols that mathematically depend on other symbols. For example, if f = Function('f') and x = Symbol('x'), then SymPy will know that f(x) depends on x, meaning, for instance, that the derivative diff(f(x), x) will not be evaluated to 0.

Unevaluated#

An expression is unevaluated if the automatic simplification that typically occurs when the expression is created is disabled. This is typically done by setting evaluate=False, using with evaluate(False), or using UnevaluatedExpr. While unevaluated expressions are supported, they can sometimes lead to surprising behavior because the expressions are not properly canonicalized.

The term unevaluated is also sometimes used to denote the fact that an expression does not evaluate to a specific value when its arguments are symbolic.

zoo#

zoo represents complex infinity, i.e., the north pole of the Riemann sphere. The reason it is spelled this way is that it is “z-oo”, where “z” is the symbol commonly used for complex variables, and oo is the symbol SymPy uses for real positive infinity.