Python: Introducción

Python is a clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.

Some of Python’s notable features:

  • Uses an elegant syntax, making the programs you write easier to read.
  • Is an easy-to-use language that makes it simple to get your program working. This makes Python ideal for prototype development and other ad-hoc programming tasks, without compromising maintainability.
  • Comes with a large standard library that supports many common programming tasks such as connecting to web servers, searching text with regular expressions, reading and modifying files.
  • Python’s interactive mode makes it easy to test short snippets of code. There’s also a bundled development environment called IDLE.
  • Is easily extended by adding new modules implemented in a compiled language such as C or C++.
  • Can also be embedded into an application to provide a programmable interface.
  • Runs on many different computers and operating systems: Windows, MacOS, many brands of Unix, OS/2, …
  • Is free software in two senses. It doesn’t cost anything to download or use Python, or to include it in your application. Python can also be freely modified and re-distributed, because while the language is copyrighted it’s available under an open source license.

Some programming-language features of Python are:

  • A variety of basic data types are available: numbers (floating point, complex, and unlimited-length long integers), strings (both ASCII and Unicode), lists, and dictionaries.
  • Python supports object-oriented programming with classes and multiple inheritance.
  • Code can be grouped into modules and packages.
  • The language supports raising and catching exceptions, resulting in cleaner error handling.
  • Data types are strongly and dynamically typed. Mixing incompatible types (e.g. attempting to add a string and a number) causes an exception to be raised, so errors are caught sooner.
  • Python contains advanced programming features such as generators and list comprehensions.
  • Python’s automatic memory management frees you from having to manually allocate and free memory in your code.

See the SimplePrograms collection of short programs, gradually increasing in length, which show off Python’s syntax and readability.


Python was conceived in the late 1980s and its implementation was started in December 1989 by Guido van Rossum at CWI in Amsterdam. Python is a successor to the ABC programming language (itself inspired by SETL) capable of exception handling and interfacing with the Amoeba operating system. Van Rossum is Python’s principal author, and his continuing central role in deciding the direction of Python is reflected in the title given to him by the Python community, Benevolent Dictator for Life (BDFL).

Programming philosophy

Python is a multi-paradigm programming language. Rather than forcing programmers to adopt a particular style of programming, it permits several styles: object-oriented programming and structured programming are fully supported, and there are a number of language features which support functional programming and aspect-oriented programming (including metaprogramming and “by magic” methods). Many other paradigms are supported using extensions, such as pyDBC and Contracts for Python which allow Design by Contract.

Rather than requiring all desired functionality to be built into the language’s core, Python was designed to be highly extensible. New built-in modules can be easily written in C, C++ or Cython. Python can also be used as an extension language for existing modules and applications that need a programmable interface. This design, a small core language with a large standard library with an easily extensible interpreter, was intended by Van Rossum from the very start because of his frustrations with ABC (which espoused the opposite mindset).

Name and neologisms

An important goal of the Python developers is making Python fun to use. This is reflected in the origin of the name (based on the television series Monty Python’s Flying Circus), in the common practice of using Monty Python references in example code, and in an occasionally playful approach to tutorials and reference materials.[24][25] For example, the metasyntactic variables often used in Python literature are spam and eggs, instead of the traditional foo and bar.



Python is often used as a scripting language for web applications, e.g. via mod_wsgi for the Apache web server. With Web Server Gateway Interface, a standard API has been developed to facilitate these applications. Web application frameworks like Django, Pylons, TurboGears, web2py, Flask and Zope support developers in the design and maintenance of complex applications. Libraries like NumPySciPy and Matplotlib allow Python to be used effectively in scientific computing.

Syntax and semantics

Python was intended to be a highly readable language. It is designed to have an uncluttered visual layout, frequently using English keywords where other languages use punctuation. Python requires less boilerplate than traditional manifestly typed structured languages such as C or Pascal, and has a smaller number of syntactic exceptions and special cases than either of these. For a detailed description of the differences between 2.x and 3.x versions, see History of Python.


Python uses whitespace indentation, rather than curly braces or keywords, to delimit blocks (a feature also known as the off-side rule). An increase in indentation comes after certain statements; a decrease in indentation signifies the end of the current block.

Statements and control flow

Python’s statements include (among others):

  • 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; it also ensures that clean-up code in a finally block will always be run regardless of how the block exits.
  • 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 (from Python 2.5), which encloses a code block within a context manager (for example, acquiring a lock before the block of code is run, and releasing the lock afterwards).
  • The pass statement, which serves as a NOP and can be used in place of a code block.
  • The assert statement, used during debugging to check for conditions that ought to apply.
  • The yield statement, which returns a value from a generator function. (From Python 2.5, yield is also an operator. This form is used to implement coroutines — see below.)


  • In Python 2, the / operator on integers does integer division: it truncates the result to an integer. Floating-point division on integers can be achieved by converting one of the integers to a float (e.g. float(x) / y).
    • In Python 3, the result of / is always a floating-point value, and a new operator // is introduced to do integer division; this behaviour can be enabled in Python 2.2+ using from __future__ import division.
  • In Python, == compares by value, in contrast to Java, where it compares by reference. (Value comparisons in Java use the equals() method.) Python’s is operator may be used to compare object identities (comparison by reference). Comparisons may be chained, for example a <= b <= c.
  • Python uses the words andornot for its boolean operators rather than the symbolic &&, ||, ! used in C.
  • Python has a type of expression known as a list comprehension. Python 2.4 extended list comprehensions into a more general expression known as a generator expression.
  • Anonymous functions are implemented using lambda expressions; however, these are limited in that the body can only be a single expression.
  • Conditional expressions in Python are written as x if c else y (different in order of operands from the ?: operator common to many other languages).
  • Python makes a distinction between lists and tuples. Lists, 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 the keys of dictionaries, provided all elements of the tuple are immutable. The parentheses around the tuple are optional in some contexts. Tuples can appear on the left side of an equal sign; hence a statement like x, y = y, x can be used to swap two variables.
  • Python 2 has a “string format” operator %. This functions analogous to printf format strings in C, e.g. “foo=%s bar=%d” % (“blah”, 2) evaluates to “foo=blah bar=2”. In Python 3, this was obsoleted in favour of the format() method of the str class, e.g. “foo={0} bar={1}”.format(“blah”, 2).
  • Python has various kinds of string literals:
    • Strings are delimited by single or double quotation marks. Unlike in Unix shells, Perl and Perl-influenced languages, single quotation marks and double quotation marks function similarly. Both kinds of string use the backslash (\) as an escape character and there is no implicit string interpolation such as “$foo”.
    • Triple-quoted strings, which begin and end with a series of three single or double quotation marks, may span multiple lines and function like here documents in shells, Perl and Ruby.


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 in some other object-oriented programming languages (for example, Java, C++ or Ruby).


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 the given object 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 defines the modulus operator so that the result of a % b is in the half-open interval [0,b), where b is a positive integer. When b is negative, the result lies in the interval (b,0]. However, this consequently affects how integer division is defined. To maintain the validity of the equation b * (a // b) + a % b == a, integer division is defined to round towards minus infinity. Therefore 7 // 3 is 2, but (−7) // 3 is −3. This is different from many programming languages, where the result of integer division rounds towards zero, and Python’s modulus operator is consequently defined in a way that can return negative numbers.



The mainstream Python implementation, known as CPython, is written in C meeting the C89 standard. CPython compiles Python programs into intermediate bytecode,[65] which are then executed by the virtual machine. It is distributed with a large standard library written in a mixture of C and Python. CPython ships in versions for many platforms, including Microsoft Windows and most modern Unix-like systems. CPython was intended from almost its very conception to be cross-platform; its use and development on esoteric platforms such as Amoeba, alongside more conventional ones like Unix and Mac OS, has greatly helped in this regard.

Alternative implementations

Jython compiles the Python program into Java byte code, which can then be executed by every Java Virtual Machine implementation. This also enables the use of Java class library functions from the Python program. IronPython follows a similar approach in order to run Python programs on the .NET Common Language Runtime. PyPy is a fast self-hosting implementation of Python, written in Python, that can output several types of bytecode, object code and intermediate languages. There also exist compilers to high-level object languages, with either unrestricted Python, a restricted subset of Python, or a language similar to Python as the source language. PyPy is of this type, compiling RPython to several languages; other examples include Pyjamas compiling to JavaScript; Shed Skin compiling to C++; and Cython and Pyrex compiling to C.

  • Python is available on Android as an option as part of the Android Scripting Environment.

Interpretational semantics

Most Python implementations (including CPython) can function as a command line interpreter, for which the user enters statements sequentially and receives the results immediately. In short, Python acts as a shell. While the semantics of the other modes of execution (bytecode compilation, or compilation to native code) preserve the sequential semantics, they offer a speed boost at the cost of interactivity, so they are usually only used outside of a command-line interaction (e.g., when importing a module).

Other shells add capabilities beyond those in the basic interpreter, including IDLE and IPython. While generally following the visual style of the Python shell, they implement features like auto-completion, retention of session state, and syntax highlighting.


Python’s development is conducted largely through the Python Enhancement Proposal (PEP) process. PEPs are standardized design documents providing general information related to Python, including proposals, descriptions, design rationales, and explanations for language features. Outstanding PEPs are reviewed and commented upon by Van Rossum, the Python project’s Benevolent Dictator for Life (leader / language architect). CPython’s developers also communicate over a mailing list, python-dev, which is the primary forum for discussion about the language’s development; specific issues are discussed in the Roundup bug tracker maintained at Development takes place at the self-hosted

CPython’s public releases come in three types, distinguished by which part of the version number is incremented:

  • backwards-incompatible versions, where code is expected to break and must be manually ported. The first part of the version number is incremented. These releases happen infrequently—for example, version 3.0 was released 8 years after 2.0.
  • major or ‘feature’ releases, which are largely compatible but introduce new features. The second part of the version number is incremented. These releases are scheduled to occur roughly every 18 months, and each major version is supported by bugfixes for several years after its release.
  • bugfix releases, which introduce no new features but fix bugs. The third and final part of the version number is incremented. These releases are made whenever a sufficient number of bugs have been fixed upstream since the last release, or roughly every 3 months. Security vulnerabilities are also patched in bugfix releases.

A number of alpha, beta, and release-candidates are also released as previews and for testing before the final release is made. Although there is a rough schedule for each release, this is often pushed back if the code is not ready. The development team monitor the state of the code by running the large unit test suite during development, and using the BuildBot continuous integration system.

Standard library

Python has a large standard library, commonly cited as one of Python’s greatest strengths,[81] providing pre-written tools suited to many tasks. This is deliberate and has been described as a “batteries included” Python philosophy. The modules of the standard library can be augmented with custom modules written in either C or Python. Boost C++ Libraries includes a library, Boost.Python, to enable interoperability between C++ and Python. Because of the wide variety of tools provided by the standard library, combined with the ability to use a lower-level language such as C and C++, which is already capable of interfacing between other libraries, Python can be a powerful glue language between languages and tools.

The standard library is particularly well tailored to writing Internet-facing applications, with a large number of standard formats and protocols (such as MIME and HTTP) already supported. Modules for creating graphical user interfaces, connecting to relational databases, arithmetic with arbitrary precision decimals, manipulating regular expressions, and doing unit testing are also included.

Some parts of the standard library are covered by specifications (for example, the WSGI implementation wsgiref follows PEP 333), but the majority of the modules are not. They are specified by their code, internal documentation, and test suite (if supplied). However, because most of the standard library is cross-platform Python code, there are only a few modules that must be altered or completely rewritten by alternative implementations.

For software testing, the standard library provides the unittest and doctest modules.

Influence on other languages

Python’s design and philosophy have influenced several programming languages, including:

  • Pyrex and its derivative Cython are code translators that are targeted at writing fast C extensions for the CPython interpreter. The language is mostly Python with syntax extensions for C and C++ features. Both languages produce compilable C code as output.
  • Boo uses indentation, a similar syntax, and a similar object model. However, Boo uses static typing and is closely integrated with the .NET framework.[84]
  • Cobra uses indentation and a similar syntax. Cobra’s “Acknowledgements” document lists Python first among languages that influenced it. However, Cobra directly supports design-by-contract, unit tests and optional static typing.
  • \ borrowed iterators, generators, and list comprehensions from Python.
  • Go is described as incorporating the “development speed of working in a dynamic language like Python”.
  • Groovy was motivated by the desire to bring the Python design philosophy to Java.
  • OCaml has an optional syntax, called twt (The Whitespace Thing), inspired by Python and Haskell.