Indentation

Python uses whitespace indentation, rather than curly brackets or keywords, to delimit blocks. An increase in indentation comes after certain statements; a decrease in indentation signifies the end of the current block.^[83] Thus, the program's visual structure accurately represents its semantic structure.^[84] This feature is sometimes termed the off-side rule. Some other languages use indentation this way; but in most, indentation has no semantic meaning. The recommended indent size is four spaces.^[85]

Statements and control flow

Python's statements include:

• The assignment statement, using a single equals sign =
• The if statement, which conditionally executes a block of code, along with else and elif (a contraction of else-if)
• The for statement, which iterates over an iterable object, capturing each element to a local variable for use by the attached block
• The while statement, which executes a block of code as long as its condition is true
• The try statement, which allows exceptions raised in its attached code block to be caught and handled by except clauses (or new syntax except* in Python 3.11 for exception groups^[86]); it also ensures that clean-up code in a finally block is always run regardless of how the block exits
• The raise statement, used to raise a specified exception or re-raise a caught exception
• The class statement, which executes a block of code and attaches its local namespace to a class, for use in object-oriented programming
• The def statement, which defines a function or method
• The with statement, which encloses a code block within a context manager (for example, acquiring a lock before it is run, then releasing the lock; or opening and closing a file), allowing resource-acquisition-is-initialization (RAII)-like behavior and replacing a common try/finally idiom^[87]
• The break statement, which exits a loop
• The continue statement, which skips the rest of the current iteration and continues with the next
• The del statement, which removes a variable—deleting the reference from the name to the value, and producing an error if the variable is referred to before it is redefined
• The pass statement, serving as a NOP, syntactically needed to create an empty code block
• The assert statement, used in debugging to check for conditions that should apply
• The yield statement, which returns a value from a generator function (and also an operator); used to implement coroutines
• The return statement, used to return a value from a function
• The import and from statements, used to import modules whose functions or variables can be used in the current program

The assignment statement (=) binds a name as a reference to a separate, dynamically allocated object. Variables may subsequently be rebound at any time to any object. In Python, a variable name is a generic reference holder without a fixed data type; however, it always refers to some object with a type. This is called dynamic typing—in contrast to statically-typed languages, where each variable may contain only a value of a certain type.

Python does not support tail call optimization or first-class continuations, and, according to Van Rossum, it never will.^[88][89] However, better support for coroutine-like functionality is provided by extending Python's generators.^[90] Before 2.5, generators were lazy iterators; data was passed unidirectionally out of the generator. From Python 2.5 on, it is possible to pass data back into a generator function; and from version 3.3, it can be passed through multiple stack levels.^[91]

Expressions

Python's expressions include:

• The +, -, and * operators for mathematical addition, subtraction, and multiplication are similar to other languages, but the behavior of division differs. There are two types of divisions in Python: floor division (or integer division) // and floating-point/division.^[92] Python uses the ** operator for exponentiation.
• Python uses the + operator for string concatenation. Python uses the * operator for duplicating a string a specified number of times.
• The @ infix operator. It is intended to be used by libraries such as NumPy for matrix multiplication.^[93][94]
• The syntax :=, called the "walrus operator", was introduced in Python 3.8. It assigns values to variables as part of a larger expression.^[95]
• In Python, == compares by value. Python's is operator may be used to compare object identities (comparison by reference), and comparisons may be chained—for example, a <= b <= c.
• Python uses and, or, and not as Boolean operators.
• Python has a type of expression called a list comprehension, as well as a more general expression called a generator expression.^[71]
• Anonymous functions are implemented using lambda expressions; however, there may be only one expression in each body.
• Conditional expressions are written as x if c else y^[96] (different in order of operands from the c ? x : y operator common to many other languages).
• Python makes a distinction between lists and tuples. Lists are written as [1, 2, 3], are mutable, and cannot be used as the keys of dictionaries (dictionary keys must be immutable in Python). Tuples, written as (1, 2, 3), are immutable and thus can be used as keys of dictionaries, provided all of the tuple's elements are immutable. The + operator can be used to concatenate two tuples, which does not directly modify their contents, but produces a new tuple containing the elements of both. Thus, given the variable t initially equal to (1, 2, 3), executing t = t + (4, 5) first evaluates t + (4, 5), which yields (1, 2, 3, 4, 5), which is then assigned back to t—thereby effectively "modifying the contents" of t while conforming to the immutable nature of tuple objects. Parentheses are optional for tuples in unambiguous contexts.^[97]
• Python features sequence unpacking where multiple expressions, each evaluating to anything that can be assigned (to a variable, writable property, etc.) are associated in an identical manner to that forming tuple literals—and, as a whole, are put on the left-hand side of the equal sign in an assignment statement. The statement expects an iterable object on the right-hand side of the equal sign that produces the same number of values as the provided writable expressions; when iterated through them, it assigns each of the produced values to the corresponding expression on the left.^[98]
• Python has a "string format" operator % that functions analogously to printf format strings in C—e.g. "spam=%s eggs=%d" % ("blah", 2) evaluates to "spam=blah eggs=2". In Python 2.6+ and 3+, this was supplemented by the format() method of the str class, e.g. "spam={0} eggs={1}".format("blah", 2). Python 3.6 added "f-strings": spam = "blah"; eggs = 2; f'spam={spam} eggs={eggs}'.^[99]
• Strings in Python can be concatenated by "adding" them (with the same operator as for adding integers and floats), e.g. "spam" + "eggs" returns "spameggs". If strings contain numbers, they are added as strings rather than integers, e.g. "2" + "2" returns "22".
• Python has various string literals:
  • Delimited by single or double quotes; unlike in Unix shells, Perl, and Perl-influenced languages, single and double quotes work the same. Both use the backslash (\) as an escape character. String interpolation became available in Python 3.6 as "formatted string literals".^[99]
  • Triple-quoted (beginning and ending with three single or double quotes), which may span multiple lines and function like here documents in shells, Perl, and Ruby.
  • Raw string varieties, denoted by prefixing the string literal with r. Escape sequences are not interpreted; hence raw strings are useful where literal backslashes are common, such as regular expressions and Windows-style paths. (Compare "@-quoting" in C#.)
• Python has array index and array slicing expressions in lists, denoted as a[key], a[start:stop] or a[start:stop:step]. Indexes are zero-based, and negative indexes are relative to the end. Slices take elements from the start index up to, but not including, the stop index. The third slice parameter called step or stride, allows elements to be skipped and reversed. Slice indexes may be omitted—for example, a[:] returns a copy of the entire list. Each element of a slice is a shallow copy.

In Python, a distinction between expressions and statements is rigidly enforced, in contrast to languages such as Common Lisp, Scheme, or Ruby. This leads to duplicating some functionality. For example:

• List comprehensions vs. for-loops
• Conditional expressions vs. if blocks
• The eval() vs. exec() built-in functions (in Python 2, exec is a statement); the former is for expressions, the latter is for statements

Statements cannot be a part of an expression—so list and other comprehensions or lambda expressions, all being expressions, cannot contain statements. A particular case is that an assignment statement such as a = 1 cannot form part of the conditional expression of a conditional statement. This has the advantage of avoiding a classic C error of mistaking an assignment operator = for an equality operator == in conditions: if (c = 1) { ... } is syntactically valid (but probably unintended) C code, but if c = 1: ... causes a syntax error in Python.

Methods

Methods on objects are functions attached to the object's class; the syntax instance.method(argument) is, for normal methods and functions, syntactic sugar for Class.method(instance, argument). Python methods have an explicit self parameter to access instance data, in contrast to the implicit self (or this) in some other object-oriented programming languages (e.g., C++, Java, Objective-C, Ruby).^[100] Python also provides methods, often called dunder methods (due to their names beginning and ending with double-underscores), to allow user-defined classes to modify how they are handled by native operations including length, comparison, in arithmetic operations and type conversion.^[101]

Typing

The standard type hierarchy in Python 3

Python uses duck typing and has typed objects but untyped variable names. Type constraints are not checked at compile time; rather, operations on an object may fail, signifying that it is not of a suitable type. Despite being dynamically typed, Python is strongly typed, forbidding operations that are not well-defined (for example, adding a number to a string) rather than silently attempting to make sense of them.

Python allows programmers to define their own types using classes, most often used for object-oriented programming. New instances of classes are constructed by calling the class (for example, SpamClass() or EggsClass()), and the classes are instances of the metaclass type (itself an instance of itself), allowing metaprogramming and reflection.

Before version 3.0, Python had two kinds of classes (both using the same syntax): old-style and new-style,^[102] current Python versions only support the semantics new style.

Python supports optional type annotations.^[4][103] These annotations are not enforced by the language, but may be used by external tools such as mypy to catch errors.^[104][105] Mypy also supports a Python compiler called mypyc, which leverages type annotations for optimization.^[106]

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Arithmetic operations

Python has the usual symbols for arithmetic operators (+, -, *, /), the floor division operator // and the modulo operation % (where the remainder can be negative, e.g. 4 % -3 == -2). It also has ** for exponentiation, e.g. 5**3 == 125 and 9**0.5 == 3.0, and a matrix‑multiplication operator @ .^[110] These operators work like in traditional math; with the same precedence rules, the operators infix (+ and - can also be unary to represent positive and negative numbers respectively).

The division between integers produces floating-point results. The behavior of division has changed significantly over time:^[111]

• Current Python (i.e. since 3.0) changed / to always be floating-point division, e.g. 5/2 == 2.5.
• The floor division // operator was introduced. So 7//3 == 2, -7//3 == -3, 7.5//3 == 2.0 and -7.5//3 == -3.0. Adding from __future__ import division causes a module used in Python 2.7 to use Python 3.0 rules for division (see above).

In Python terms, / is true division (or simply division), and // is floor division. / before version 3.0 is classic division.^[111]

Rounding towards negative infinity, though different from most languages, adds consistency. For instance, it means that the equation (a + b)//b == a//b + 1 is always true. It also means that the equation b*(a//b) + a%b == a is valid for both positive and negative values of a. However, maintaining the validity of this equation means that while the result of a%b is, as expected, in the half-open interval [0, b), where b is a positive integer, it has to lie in the interval (b, 0] when b is negative.^[112]

Python provides a round function for rounding a float to the nearest integer. For tie-breaking, Python 3 uses round to even: round(1.5) and round(2.5) both produce 2.^[113] Versions before 3 used round-away-from-zero: round(0.5) is 1.0, round(-0.5) is −1.0.^[114]

Python allows Boolean expressions with multiple equality relations in a manner that is consistent with general use in mathematics. For example, the expression a < b < c tests whether a is less than b and b is less than c.^[115] C-derived languages interpret this expression differently: in C, the expression would first evaluate a < b, resulting in 0 or 1, and that result would then be compared with c.^[116]

Python uses arbitrary-precision arithmetic for all integer operations. The Decimal type/class in the decimal module provides decimal floating-point numbers to a pre-defined arbitrary precision and several rounding modes.^[117] The Fraction class in the fractions module provides arbitrary precision for rational numbers.^[118]

Due to Python's extensive mathematics library, and the third-party library NumPy that further extends the native capabilities, it is frequently used as a scientific scripting language to aid in problems such as numerical data processing and manipulation.^[119][120]

Reference implementation

CPython is the reference implementation of Python. It is written in C, meeting the C89 standard (Python 3.11 uses C11^[126]) with several select C99 features. CPython includes its own C extensions, but third-party extensions are not limited to older C versions—e.g. they can be implemented with C11 or C++.^[127][128]) It compiles Python programs into an intermediate bytecode^[129] which is then executed by its virtual machine.^[130] CPython is distributed with a large standard library written in a mixture of C and native Python, and is available for many platforms, including Windows (starting with Python 3.9, the Python installer deliberately fails to install on Windows 7 and 8;^[131][132] Windows XP was supported until Python 3.5) and most modern Unix-like systems, including macOS (and Apple M1 Macs, since Python 3.9.1, with experimental installer) and unofficial support for e.g. VMS.^[133] Platform portability was one of its earliest priorities.^[134] (During Python 1 and 2 development, even OS/2 and Solaris were supported,^[135] but support has since been dropped for many platforms.)

Other implementations

• PyPy is a fast, compliant interpreter of Python 2.7 and 3.8.^[136][137] Its just-in-time compiler often brings a significant speed improvement over CPython but some libraries written in C cannot be used with it.^[138]
• Stackless Python is a significant fork of CPython that implements microthreads; it does not use the call stack in the same way, thus allowing massively concurrent programs. PyPy also has a stackless version.^[139]
• MicroPython and CircuitPython are Python 3 variants optimized for microcontrollers, including Lego Mindstorms EV3.^[140]
• Pyston is a variant of the Python runtime that uses just-in-time compilation to speed up the execution of Python programs.^[141]
• Cinder is a performance-oriented fork of CPython 3.8 that contains a number of optimizations, including bytecode inline caching, eager evaluation of coroutines, a method-at-a-time JIT, and an experimental bytecode compiler.^[142]
• Snek^{[143][144][145]} Embedded Computing Language (supporting e.g. 8-bit AVR microcontrollers such as ATmega 328P-based Arduino, and larger ones that are MicroPython can also support) "is Python-inspired, but it is not Python. It is possible to write Snek programs that run under a full Python system, but most Python programs will not run under Snek." It's an imperative language not including OOP/classes unlike Python, and simplifying to one number type (like JavaScript, except using smaller) 32-bit single-precision "Integer values of less than 24 bits can be represented exactly in these floating point values".^[146]

Unsupported implementations

Other just-in-time Python compilers have been developed, but are now unsupported:

• Google began a project named Unladen Swallow in 2009, with the aim of speeding up the Python interpreter fivefold by using the LLVM, and of improving its multithreading ability to scale to thousands of cores,^[147] while ordinary implementations suffer from the global interpreter lock.
• Psyco is a discontinued just-in-time specializing compiler that integrates with CPython and transforms bytecode to machine code at runtime. The emitted code is specialized for certain data types and is faster than the standard Python code. Psyco does not support Python 2.7 or later.
• PyS60 was a Python 2 interpreter for Series 60 mobile phones released by Nokia in 2005. It implemented many of the modules from the standard library and some additional modules for integrating with the Symbian operating system. The Nokia N900 also supports Python with GTK widget libraries, enabling programs to be written and run on the target device.^[148]

Cross-compilers to other languages

There are several compilers/transpilers to high-level object languages, with either unrestricted Python, a restricted subset of Python, or a language similar to Python as the source language:

• Brython,^[149] Transcrypt^[150][151] and Pyjs (latest release in 2012) compile Python to JavaScript.
• Codon compiles a subset of statically typed Python^[152] to machine code (via LLVM) and supports native multithreading.^[153]
• Cython compiles (a superset of) Python to C. The resulting code is also usable with Python via direct C-level API calls into the Python interpreter.
• PyJL compiles/transpiles a subset of Python to "human-readable, maintainable, and high-performance Julia source code".^[76] Despite claiming high performance, no tool can claim to do that for arbitrary Python code; i.e. it's known not possible to compile to a faster language or machine code. Unless semantics of Python are changed, but in many cases speedup is possible with few or no changes in the Python code. The faster Julia source code can then be used from Python, or compiled to machine code, and based that way.
• Nuitka compiles Python into C.^[154]
• Numba uses LLVM to compile a subset of Python to machine code.
• Pythran compiles a subset of Python 3 to C++ (C++11).^[155]
• RPython can be compiled to C, and is used to build the PyPy interpreter of Python.
• The Python → 11l → C++ transpiler^[156] compiles a subset of Python 3 to C++ (C++17).

Specialized:

• MyHDL is a Python-based hardware description language (HDL), that converts MyHDL code to Verilog or VHDL code.

Older projects (or not to be used with Python 3.x and latest syntax):

• Google's Grumpy (latest release in 2017) transpiles Python 2 to Go.^{[157][158][159]}
• IronPython allows running Python 2.7 programs (and an alpha, released in 2021, is also available for "Python 3.4, although features and behaviors from later versions may be included"^[160]) on the .NET Common Language Runtime.^[161]
• Jython compiles Python 2.7 to Java bytecode, allowing the use of the Java libraries from a Python program.^[162]
• Pyrex (latest release in 2010) and Shed Skin (latest release in 2013) compile to C and C++ respectively.

Performance

Performance comparison of various Python implementations on a non-numerical (combinatorial) workload was presented at EuroSciPy '13.^[163] Python's performance compared to other programming languages is also benchmarked by The Computer Language Benchmarks Game.^[164]

Sources

• "Python for Artificial Intelligence". Python Wiki. 19 July 2012. Archived from the original on 1 November 2012. Retrieved 3 December 2012.
• Paine, Jocelyn, ed. (August 2005). "AI in Python". AI Expert Newsletter. Amzi!. Archived from the original on 26 March 2012. Retrieved 11 February 2012.
• "PyAIML 0.8.5 : Python Package Index". Pypi.python.org. Retrieved 17 July 2013.
• Russell, Stuart J. & Norvig, Peter (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall. ISBN 978-0-13-604259-4.