Learn Python

Learn python in 7 hours! Python Lessons A complete course of learning to program in python from scratch

  1.   00:00  Introduction to the course of python lessons
  2.   00:45  Installing python, the first program
  3.   10:47  Variables, Variables w3s
  4.   20:56  Data types in pytho
  5.   30:02  Conditional statements if, elif, else
  6.   39:03  Conditional statements 2
  7.   45:53  Conditional Operators Practice
  8.   56:44  while loop
  9.   1:12:37  for loop
  10.   1:21:23  A for loop, a loop within a loop
  11.   1:29:34  Lists, list data type
  12.   1:41:29  Lists, methods of the list data type
  13.   1:53:21  Tuples, tuple data type
  14.   2:08:42  Practice python, os module, walk function
  15.   2:21:16  def functions, definition, and call
  16.   2:30:07  def functions, parameters, and arguments
  17.   2:43:49  Functions variable number of arguments, *args parameter
  18.   2:56:28  Functions, variable scope
  19.   3:08:49  Functions and code structure
  20.   3:22:07  Dictionaries, data type dict
  21.   3:37:22  Dictionaries, dict methods
  22.   3:49:21  Reading and writing files
  23.   4:05:29  Sets, set data type
  24.   4:22:30  Strings escaped characters
  25.   4:30:17  Strings, str methods
  26.   4:43:13  f-string, string formatting
  27.   4:50:10  Exception handling, try, except
  28.   5:09:59  Context manager with as
  29.   5:16:19  Import modules, if __name__ == ‘__main__”
  30.   5:34:58  Dice game in python Tkinter
  31.   5:56:53  Decorators
  32.   6:10:36  List, dictionary, set generators
  33.   6:30:56  Generator expression
  34.   6:43:03  Generator function, the yield statement   
  35.   6:51:51  lambda function

Python lessons for beginners from scratch – a complete course for learning to program in Python. Python is a popular programming language. Python can be used on a server to create web applications. Learn the python programming language for free, basics from scratch, complete python learning lecture course. The course video covers all the main topics with examples and practice necessary for a junior python developer. At the end of the course, you will know all the basic python syntax and be free to write your own code!

From Python.org

  • 1. Whetting Your Appetite
  • 2. Using the Python Interpreter
    • 2.1. Invoking the Interpreter
      • 2.1.1. Argument Passing
      • 2.1.2. Interactive Mode
    • 2.2. The Interpreter and Its Environment
      • 2.2.1. Source Code Encoding
  • 3. An Informal Introduction to Python
    • 3.1. Using Python as a Calculator
      • 3.1.1. Numbers
      • 3.1.2. Strings
      • 3.1.3. Lists
    • 3.2. First Steps Towards Programming
  • 4. More Control Flow Tools
    • 4.1. if Statements
    • 4.2. for Statements
    • 4.3. The range() Function
    • 4.4. break and continue Statements, and else Clauses on Loops
    • 4.5. pass Statements
    • 4.6. match Statements
    • 4.7. Defining Functions
    • 4.8. More on Defining Functions
      • 4.8.1. Default Argument Values
      • 4.8.2. Keyword Arguments
      • 4.8.3. Special parameters
        • Positional-or-Keyword Arguments
        • Positional-Only Parameters
        • Keyword-Only Arguments
        • Function Examples
        • Recap
      • 4.8.4. Arbitrary Argument Lists
      • 4.8.5. Unpacking Argument Lists
      • 4.8.6. Lambda Expressions
      • 4.8.7. Documentation Strings
      • 4.8.8. Function Annotations
    • 4.9. Intermezzo: Coding Style
  • 5. Data Structures
    • 5.1. More on Lists
      • 5.1.1. Using Lists as Stacks
      • 5.1.2. Using Lists as Queues
      • 5.1.3. List Comprehensions
      • 5.1.4. Nested List Comprehensions
    • 5.2. The del statement
    • 5.3. Tuples and Sequences
    • 5.4. Sets
    • 5.5. Dictionaries
    • 5.6. Looping Techniques
    • 5.7. More on Conditions
    • 5.8. Comparing Sequences and Other Types
  • 6. Modules
    • 6.1. More on Modules
      • 6.1.1. Executing modules as scripts
      • 6.1.2. The Module Search Path
      • 6.1.3. “Compiled” Python files
    • 6.2. Standard Modules
    • 6.3. The dir() Function
    • 6.4. Packages
      • 6.4.1. Importing * From a Package
      • 6.4.2. Intra-package References
      • 6.4.3. Packages in Multiple Directories
  • 7. Input and Output
    • 7.1. Fancier Output Formatting
      • 7.1.1. Formatted String Literals
      • 7.1.2. The String format() Method
      • 7.1.3. Manual String Formatting
      • 7.1.4. Old string formatting
    • 7.2. Reading and Writing Files
      • 7.2.1. Methods of File Objects
      • 7.2.2. Saving structured data with json
  • 8. Errors and Exceptions
    • 8.1. Syntax Errors
    • 8.2. Exceptions
    • 8.3. Handling Exceptions
    • 8.4. Raising Exceptions
    • 8.5. Exception Chaining
    • 8.6. User-defined Exceptions
    • 8.7. Defining Clean-up Actions
    • 8.8. Predefined Clean-up Actions
  • 9. Classes
    • 9.1. A Word About Names and Objects
    • 9.2. Python Scopes and Namespaces
      • 9.2.1. Scopes and Namespaces Example
    • 9.3. A First Look at Classes
      • 9.3.1. Class Definition Syntax
      • 9.3.2. Class Objects
      • 9.3.3. Instance Objects
      • 9.3.4. Method Objects
      • 9.3.5. Class and Instance Variables
    • 9.4. Random Remarks
    • 9.5. Inheritance
      • 9.5.1. Multiple Inheritance
    • 9.6. Private Variables
    • 9.7. Odds and Ends
    • 9.8. Iterators
    • 9.9. Generators
    • 9.10. Generator Expressions
  • 10. Brief Tour of the Standard Library
    • 10.1. Operating System Interface
    • 10.2. File Wildcards
    • 10.3. Command Line Arguments
    • 10.4. Error Output Redirection and Program Termination
    • 10.5. String Pattern Matching
    • 10.6. Mathematics
    • 10.7. Internet Access
    • 10.8. Dates and Times
    • 10.9. Data Compression
    • 10.10. Performance Measurement
    • 10.11. Quality Control
    • 10.12. Batteries Included
  • 11. Brief Tour of the Standard Library — Part II
    • 11.1. Output Formatting
    • 11.2. Templating
    • 11.3. Working with Binary Data Record Layouts
    • 11.4. Multi-threading
    • 11.5. Logging
    • 11.6. Weak References
    • 11.7. Tools for Working with Lists
    • 11.8. Decimal Floating Point Arithmetic
  • 12. Virtual Environments and Packages
    • 12.1. Introduction
    • 12.2. Creating Virtual Environments
    • 12.3. Managing Packages with pip
  • 13. What Now?
  • 14. Interactive Input Editing and History Substitution
    • 14.1. Tab Completion and History Editing
    • 14.2. Alternatives to the Interactive Interpreter
  • 15. Floating Point Arithmetic: Issues and Limitations
    • 15.1. Representation Error
  • 16. Appendix
    • 16.1. Interactive Mode
      • 16.1.1. Error Handling
      • 16.1.2. Executable Python Scripts
      • 16.1.3. The Interactive Startup File
      • 16.1.4. The Customization Modules

The Python Standard Library

While The Python Language Reference describes the exact syntax and semantics of the Python language, this library reference manual describes the standard library that is distributed with Python. It also describes some of the optional components that are commonly included in Python distributions.

Python’s standard library is very extensive, offering a wide range of facilities as indicated by the long table of contents listed below. The library contains built-in modules (written in C) that provide access to system functionality such as file I/O that would otherwise be inaccessible to Python programmers, as well as modules written in Python that provide standardized solutions for many problems that occur in everyday programming. Some of these modules are explicitly designed to encourage and enhance the portability of Python programs by abstracting away platform-specifics into platform-neutral APIs.

The Python installers for the Windows platform usually include the entire standard library and often also include many additional components. For Unix-like operating systems Python is normally provided as a collection of packages, so it may be necessary to use the packaging tools provided with the operating system to obtain some or all of the optional components.

In addition to the standard library, there is a growing collection of several thousand components (from individual programs and modules to packages and entire application development frameworks), available from the Python Package Index.

The Python Language Reference

This reference manual describes the syntax and “core semantics” of the language. It is terse, but attempts to be exact and complete. The semantics of non-essential built-in object types and of the built-in functions and modules are described in The Python Standard Library. For an informal introduction to the language, see The Python Tutorial. For C or C++ programmers, two additional manuals exist: Extending and Embedding the Python Interpreter describes the high-level picture of how to write a Python extension module, and the Python/C API Reference Manual describes the interfaces available to C/C++ programmers in detail.

Python Setup and Usage

This part of the documentation is devoted to general information on the setup of the Python environment on different platforms, the invocation of the interpreter and things that make working with Python easier.

Python HOWTOs

Python HOWTOs are documents that cover a single, specific topic, and attempt to cover it fairly completely. Modelled on the Linux Documentation Project’s HOWTO collection, this collection is an effort to foster documentation that’s more detailed than the Python Library Reference.

Currently, the HOWTOs are:

Installing Python Modules


[email protected]

As a popular open source development project, Python has an active supporting community of contributors and users that also make their software available for other Python developers to use under open source license terms.

This allows Python users to share and collaborate effectively, benefiting from the solutions others have already created to common (and sometimes even rare!) problems, as well as potentially contributing their own solutions to the common pool.

This guide covers the installation part of the process. For a guide to creating and sharing your own Python projects, refer to the distribution guide.



For corporate and other institutional users, be aware that many organisations have their own policies around using and contributing to open source software. Please take such policies into account when making use of the distribution and installation tools provided with Python.

Key terms

  • pip is the preferred installer program. Starting with Python 3.4, it is included by default with the Python binary installers.

  • virtual environment is a semi-isolated Python environment that allows packages to be installed for use by a particular application, rather than being installed system wide.

  • venv is the standard tool for creating virtual environments, and has been part of Python since Python 3.3. Starting with Python 3.4, it defaults to installing pip into all created virtual environments.

  • virtualenv is a third party alternative (and predecessor) to venv. It allows virtual environments to be used on versions of Python prior to 3.4, which either don’t provide venv at all, or aren’t able to automatically install pip into created environments.

  • The Python Package Index is a public repository of open source licensed packages made available for use by other Python users.

  • the Python Packaging Authority is the group of developers and documentation authors responsible for the maintenance and evolution of the standard packaging tools and the associated metadata and file format standards. They maintain a variety of tools, documentation, and issue trackers on both GitHub and Bitbucket.

  • distutils is the original build and distribution system first added to the Python standard library in 1998. While direct use of distutils is being phased out, it still laid the foundation for the current packaging and distribution infrastructure, and it not only remains part of the standard library, but its name lives on in other ways (such as the name of the mailing list used to coordinate Python packaging standards development).

Changed in version 3.5: The use of venv is now recommended for creating virtual environments.

Basic usage

The standard packaging tools are all designed to be used from the command line.

The following command will install the latest version of a module and its dependencies from the Python Package Index:

python -m pip install SomePackage



For POSIX users (including macOS and Linux users), the examples in this guide assume the use of a virtual environment.

For Windows users, the examples in this guide assume that the option to adjust the system PATH environment variable was selected when installing Python.

It’s also possible to specify an exact or minimum version directly on the command line. When using comparator operators such as >< or some other special character which get interpreted by shell, the package name and the version should be enclosed within double quotes:

python -m pip install SomePackage==1.0.4    # specific version
python -m pip install "SomePackage>=1.0.4"  # minimum version

Normally, if a suitable module is already installed, attempting to install it again will have no effect. Upgrading existing modules must be requested explicitly:

python -m pip install --upgrade SomePackage

More information and resources regarding pip and its capabilities can be found in the Python Packaging User Guide.

Creation of virtual environments is done through the venv module. Installing packages into an active virtual environment uses the commands shown above.

How do I …?

These are quick answers or links for some common tasks.

… install pip in versions of Python prior to Python 3.4?

Python only started bundling pip with Python 3.4. For earlier versions, pip needs to be “bootstrapped” as described in the Python Packaging User Guide.

… install packages just for the current user?

Passing the --user option to python -m pip install will install a package just for the current user, rather than for all users of the system.

… install scientific Python packages?

A number of scientific Python packages have complex binary dependencies, and aren’t currently easy to install using pip directly. At this point in time, it will often be easier for users to install these packages by other means rather than attempting to install them with pip.

… work with multiple versions of Python installed in parallel?

On Linux, macOS, and other POSIX systems, use the versioned Python commands in combination with the -m switch to run the appropriate copy of pip:

python2   -m pip install SomePackage  # default Python 2
python2.7 -m pip install SomePackage  # specifically Python 2.7
python3   -m pip install SomePackage  # default Python 3
python3.4 -m pip install SomePackage  # specifically Python 3.4

Appropriately versioned pip commands may also be available.

On Windows, use the py Python launcher in combination with the -m switch:

py -2   -m pip install SomePackage  # default Python 2
py -2.7 -m pip install SomePackage  # specifically Python 2.7
py -3   -m pip install SomePackage  # default Python 3
py -3.4 -m pip install SomePackage  # specifically Python 3.4

Common installation issues

Installing into the system Python on Linux

On Linux systems, a Python installation will typically be included as part of the distribution. Installing into this Python installation requires root access to the system, and may interfere with the operation of the system package manager and other components of the system if a component is unexpectedly upgraded using pip.

On such systems, it is often better to use a virtual environment or a per-user installation when installing packages with pip.

Pip not installed

It is possible that pip does not get installed by default. One potential fix is:

python -m ensurepip --default-pip

There are also additional resources for installing pip.

Installing binary extensions

Python has typically relied heavily on source based distribution, with end users being expected to compile extension modules from source as part of the installation process.

With the introduction of support for the binary wheel format, and the ability to publish wheels for at least Windows and macOS through the Python Package Index, this problem is expected to diminish over time, as users are more regularly able to install pre-built extensions rather than needing to build them themselves.

Some of the solutions for installing scientific software that are not yet available as pre-built wheel files may also help with obtaining other binary extensions without needing to build them locally.

Distributing Python Modules


[email protected]

As a popular open source development project, Python has an active supporting community of contributors and users that also make their software available for other Python developers to use under open source license terms.

This allows Python users to share and collaborate effectively, benefiting from the solutions others have already created to common (and sometimes even rare!) problems, as well as potentially contributing their own solutions to the common pool.

This guide covers the distribution part of the process. For a guide to installing other Python projects, refer to the installation guide.



For corporate and other institutional users, be aware that many organisations have their own policies around using and contributing to open source software. Please take such policies into account when making use of the distribution and installation tools provided with Python.

Key terms

  • the Python Package Index is a public repository of open source licensed packages made available for use by other Python users

  • the Python Packaging Authority are the group of developers and documentation authors responsible for the maintenance and evolution of the standard packaging tools and the associated metadata and file format standards. They maintain a variety of tools, documentation and issue trackers on both GitHub and Bitbucket.

  • distutils is the original build and distribution system first added to the Python standard library in 1998. While direct use of distutils is being phased out, it still laid the foundation for the current packaging and distribution infrastructure, and it not only remains part of the standard library, but its name lives on in other ways (such as the name of the mailing list used to coordinate Python packaging standards development).

  • setuptools is a (largely) drop-in replacement for distutils first published in 2004. Its most notable addition over the unmodified distutils tools was the ability to declare dependencies on other packages. It is currently recommended as a more regularly updated alternative to distutils that offers consistent support for more recent packaging standards across a wide range of Python versions.

  • wheel (in this context) is a project that adds the bdist_wheel command to distutils/setuptools. This produces a cross platform binary packaging format (called “wheels” or “wheel files” and defined in PEP 427) that allows Python libraries, even those including binary extensions, to be installed on a system without needing to be built locally.

Open source licensing and collaboration

In most parts of the world, software is automatically covered by copyright. This means that other developers require explicit permission to copy, use, modify and redistribute the software.

Open source licensing is a way of explicitly granting such permission in a relatively consistent way, allowing developers to share and collaborate efficiently by making common solutions to various problems freely available. This leaves many developers free to spend more time focusing on the problems that are relatively unique to their specific situation.

The distribution tools provided with Python are designed to make it reasonably straightforward for developers to make their own contributions back to that common pool of software if they choose to do so.

The same distribution tools can also be used to distribute software within an organisation, regardless of whether that software is published as open source software or not.

Installing the tools

The standard library does not include build tools that support modern Python packaging standards, as the core development team has found that it is important to have standard tools that work consistently, even on older versions of Python.

The currently recommended build and distribution tools can be installed by invoking the pip module at the command line:

python -m pip install setuptools wheel twine



For POSIX users (including macOS and Linux users), these instructions assume the use of a virtual environment.

For Windows users, these instructions assume that the option to adjust the system PATH environment variable was selected when installing Python.

The Python Packaging User Guide includes more details on the currently recommended tools.

Reading the Python Packaging User Guide

The Python Packaging User Guide covers the various key steps and elements involved in creating and publishing a project:

How do I…?

These are quick answers or links for some common tasks.

… choose a name for my project?

This isn’t an easy topic, but here are a few tips:

  • check the Python Package Index to see if the name is already in use

  • check popular hosting sites like GitHub, Bitbucket, etc to see if there is already a project with that name

  • check what comes up in a web search for the name you’re considering

  • avoid particularly common words, especially ones with multiple meanings, as they can make it difficult for users to find your software when searching for it

… create and distribute binary extensions?

This is actually quite a complex topic, with a variety of alternatives available depending on exactly what you’re aiming to achieve. See the Python Packaging User Guide for more information and recommendations.

Extending and Embedding the Python Interpreter

This document describes how to write modules in C or C++ to extend the Python interpreter with new modules. Those modules can not only define new functions but also new object types and their methods. The document also describes how to embed the Python interpreter in another application, for use as an extension language. Finally, it shows how to compile and link extension modules so that they can be loaded dynamically (at run time) into the interpreter, if the underlying operating system supports this feature.

This document assumes basic knowledge about Python. For an informal introduction to the language, see The Python TutorialThe Python Language Reference gives a more formal definition of the language. The Python Standard Library documents the existing object types, functions and modules (both built-in and written in Python) that give the language its wide application range.

For a detailed description of the whole Python/C API, see the separate Python/C API Reference Manual.

Creating extensions without third party tools

This section of the guide covers creating C and C++ extensions without assistance from third party tools. It is intended primarily for creators of those tools, rather than being a recommended way to create your own C extensions.

Embedding the CPython runtime in a larger application

Sometimes, rather than creating an extension that runs inside the Python interpreter as the main application, it is desirable to instead embed the CPython runtime inside a larger application. This section covers some of the details involved in doing that successfully.

Python/C API Reference Manual

This manual documents the API used by C and C++ programmers who want to write extension modules or embed Python. It is a companion to Extending and Embedding the Python Interpreter, which describes the general principles of extension writing but does not document the API functions in detail.

Python Module Index

_ | a | b | c | d | e | f | g | h | i | j | k | l | m | n | o | p | q | r | s | t | u | v | w | x | z
 __future__Future statement definitions
 __main__The environment where top-level code is run. Covers command-line interfaces, import-time behavior, and “__name__ == ‘__main__’“.
 _threadLow-level threading API.
 abcAbstract base classes according to :pep:`3119`.
 aifcDeprecated: Read and write audio files in AIFF or AIFC format.
 argparseCommand-line option and argument parsing library.
 arraySpace efficient arrays of uniformly typed numeric values.
 astAbstract Syntax Tree classes and manipulation.
 asynchatDeprecated: Support for asynchronous command/response protocols.
 asyncioAsynchronous I/O.
 asyncoreDeprecated: A base class for developing asynchronous socket handling services.
 atexitRegister and execute cleanup functions.
 audioopDeprecated: Manipulate raw audio data.
 base64RFC 4648: Base16, Base32, Base64 Data Encodings; Base85 and Ascii85
 bdbDebugger framework.
 binasciiTools for converting between binary and various ASCII-encoded binary representations.
 binhexEncode and decode files in binhex4 format.
 bisectArray bisection algorithms for binary searching.
 builtinsThe module that provides the built-in namespace.
 bz2Interfaces for bzip2 compression and decompression.
 calendarFunctions for working with calendars, including some emulation of the Unix cal program.
 cgiDeprecated: Helpers for running Python scripts via the Common Gateway Interface.
 cgitbDeprecated: Configurable traceback handler for CGI scripts.
 chunkDeprecated: Module to read IFF chunks.
 cmathMathematical functions for complex numbers.
 cmdBuild line-oriented command interpreters.
 codeFacilities to implement read-eval-print loops.
 codecsEncode and decode data and streams.
 codeopCompile (possibly incomplete) Python code.
-collectionsContainer datatypes
 colorsysConversion functions between RGB and other color systems.
 compileallTools for byte-compiling all Python source files in a directory tree.
 configparserConfiguration file parser.
 contextlibUtilities for with-statement contexts.
 contextvarsContext Variables
 copyShallow and deep copy operations.
 copyregRegister pickle support functions.
 crypt (Unix)Deprecated: The crypt() function used to check Unix passwords.
 csvWrite and read tabular data to and from delimited files.
 ctypesA foreign function library for Python.
-curses (Unix)An interface to the curses library, providing portable terminal handling.
 dataclassesGenerate special methods on user-defined classes.
 datetimeBasic date and time types.
-dbmInterfaces to various Unix “database” formats.
 decimalImplementation of the General Decimal Arithmetic Specification.
 difflibHelpers for computing differences between objects.
 disDisassembler for Python bytecode.
-distutilsSupport for building and installing Python modules into an existing Python installation.
 doctestTest pieces of code within docstrings.
-emailPackage supporting the parsing, manipulating, and generating email messages.
 ensurepipBootstrapping the “pip” installer into an existing Python installation or virtual environment.
 enumImplementation of an enumeration class.
 errnoStandard errno system symbols.
 faulthandlerDump the Python traceback.
 fcntl (Unix)The fcntl() and ioctl() system calls.
 filecmpCompare files efficiently.
 fileinputLoop over standard input or a list of files.
 fnmatchUnix shell style filename pattern matching.
 fractionsRational numbers.
 ftplibFTP protocol client (requires sockets).
 functoolsHigher-order functions and operations on callable objects.
 gcInterface to the cycle-detecting garbage collector.
 getoptPortable parser for command line options; support both short and long option names.
 getpassPortable reading of passwords and retrieval of the userid.
 gettextMultilingual internationalization services.
 globUnix shell style pathname pattern expansion.
 graphlibFunctionality to operate with graph-like structures
 grp (Unix)The group database (getgrnam() and friends).
 gzipInterfaces for gzip compression and decompression using file objects.
 hashlibSecure hash and message digest algorithms.
 heapqHeap queue algorithm (a.k.a. priority queue).
 hmacKeyed-Hashing for Message Authentication (HMAC) implementation
-htmlHelpers for manipulating HTML.
-httpHTTP status codes and messages
 imaplibIMAP4 protocol client (requires sockets).
 imghdrDeprecated: Determine the type of image contained in a file or byte stream.
 impDeprecated: Access the implementation of the import statement.
-importlibThe implementation of the import machinery.
 inspectExtract information and source code from live objects.
 ioCore tools for working with streams.
 ipaddressIPv4/IPv6 manipulation library.
 itertoolsFunctions creating iterators for efficient looping.
-jsonEncode and decode the JSON format.
 keywordTest whether a string is a keyword in Python.
 lib2to3The 2to3 library
 linecacheProvides random access to individual lines from text files.
 localeInternationalization services.
-loggingFlexible event logging system for applications.
 lzmaA Python wrapper for the liblzma compression library.
 mailboxManipulate mailboxes in various formats
 mailcapDeprecated: Mailcap file handling.
 marshalConvert Python objects to streams of bytes and back (with different constraints).
 mathMathematical functions (sin() etc.).
 mimetypesMapping of filename extensions to MIME types.
 mmapInterface to memory-mapped files for Unix and Windows.
 modulefinderFind modules used by a script.
 msilib (Windows)Deprecated: Creation of Microsoft Installer files, and CAB files.
 msvcrt (Windows)Miscellaneous useful routines from the MS VC++ runtime.
-multiprocessingProcess-based parallelism.
 netrcLoading of .netrc files.
 nis (Unix)Deprecated: Interface to Sun’s NIS (Yellow Pages) library.
 nntplibDeprecated: NNTP protocol client (requires sockets).
 numbersNumeric abstract base classes (Complex, Real, Integral, etc.).
 operatorFunctions corresponding to the standard operators.
 optparseDeprecated: Command-line option parsing library.
-osMiscellaneous operating system interfaces.
 ossaudiodev (Linux, FreeBSD)Deprecated: Access to OSS-compatible audio devices.
 pathlibObject-oriented filesystem paths
 pdbThe Python debugger for interactive interpreters.
 pickleConvert Python objects to streams of bytes and back.
 pickletoolsContains extensive comments about the pickle protocols and pickle-machine opcodes, as well as some useful functions.
 pipes (Unix)Deprecated: A Python interface to Unix shell pipelines.
 pkgutilUtilities for the import system.
 platformRetrieves as much platform identifying data as possible.
 plistlibGenerate and parse Apple plist files.
 poplibPOP3 protocol client (requires sockets).
 posix (Unix)The most common POSIX system calls (normally used via module os).
 pprintData pretty printer.
 profilePython source profiler.
 pstatsStatistics object for use with the profiler.
 pty (Unix)Pseudo-Terminal Handling for Unix.
 pwd (Unix)The password database (getpwnam() and friends).
 py_compileGenerate byte-code files from Python source files.
 pyclbrSupports information extraction for a Python module browser.
 pydocDocumentation generator and online help system.
 queueA synchronized queue class.
 quopriEncode and decode files using the MIME quoted-printable encoding.
 randomGenerate pseudo-random numbers with various common distributions.
 reRegular expression operations.
 readline (Unix)GNU readline support for Python.
 reprlibAlternate repr() implementation with size limits.
 resource (Unix)An interface to provide resource usage information on the current process.
 rlcompleterPython identifier completion, suitable for the GNU readline library.
 runpyLocate and run Python modules without importing them first.
 schedGeneral purpose event scheduler.
 secretsGenerate secure random numbers for managing secrets.
 selectWait for I/O completion on multiple streams.
 selectorsHigh-level I/O multiplexing.
 shelvePython object persistence.
 shlexSimple lexical analysis for Unix shell-like languages.
 shutilHigh-level file operations, including copying.
 signalSet handlers for asynchronous events.
 siteModule responsible for site-specific configuration.
 smtpdDeprecated: A SMTP server implementation in Python.
 smtplibSMTP protocol client (requires sockets).
 sndhdrDeprecated: Determine type of a sound file.
 socketLow-level networking interface.
 socketserverA framework for network servers.
 spwd (Unix)Deprecated: The shadow password database (getspnam() and friends).
 sqlite3A DB-API 2.0 implementation using SQLite 3.x.
 sslTLS/SSL wrapper for socket objects
 statUtilities for interpreting the results of os.stat(), os.lstat() and os.fstat().
 statisticsMathematical statistics functions
 stringCommon string operations.
 stringprepString preparation, as per RFC 3453
 structInterpret bytes as packed binary data.
 subprocessSubprocess management.
 sunauDeprecated: Provide an interface to the Sun AU sound format.
 symtableInterface to the compiler’s internal symbol tables.
 sysAccess system-specific parameters and functions.
 sysconfigPython’s configuration information
 syslog (Unix)An interface to the Unix syslog library routines.
 tabnannyTool for detecting white space related problems in Python source files in a directory tree.
 tarfileRead and write tar-format archive files.
 telnetlibDeprecated: Telnet client class.
 tempfileGenerate temporary files and directories.
 termios (Unix)POSIX style tty control.
-testRegression tests package containing the testing suite for Python.
 textwrapText wrapping and filling
 threadingThread-based parallelism.
 timeTime access and conversions.
 timeitMeasure the execution time of small code snippets.
-tkinterInterface to Tcl/Tk for graphical user interfaces
 tokenConstants representing terminal nodes of the parse tree.
 tokenizeLexical scanner for Python source code.
 traceTrace or track Python statement execution.
 tracebackPrint or retrieve a stack traceback.
 tracemallocTrace memory allocations.
 tty (Unix)Utility functions that perform common terminal control operations.
 turtleAn educational framework for simple graphics applications
 turtledemoA viewer for example turtle scripts
 typesNames for built-in types.
 typingSupport for type hints (see :pep:`484`).
 unicodedataAccess the Unicode Database.
-unittestUnit testing framework for Python.
 uuDeprecated: Encode and decode files in uuencode format.
 uuidUUID objects (universally unique identifiers) according to RFC 4122
 venvCreation of virtual environments.
 warningsIssue warning messages and control their disposition.
 waveProvide an interface to the WAV sound format.
 weakrefSupport for weak references and weak dictionaries.
 webbrowserEasy-to-use controller for web browsers.
 winreg (Windows)Routines and objects for manipulating the Windows registry.
 winsound (Windows)Access to the sound-playing machinery for Windows.
-wsgirefWSGI Utilities and Reference Implementation.
 xdrlibDeprecated: Encoders and decoders for the External Data Representation (XDR).
-xmlPackage containing XML processing modules
 zipappManage executable Python zip archives
 zipfileRead and write ZIP-format archive files.
 zipimportSupport for importing Python modules from ZIP archives.
 zlibLow-level interface to compression and decompression routines compatible with gzip.
 zoneinfoIANA time zone support


Index pages by letter:

Symbols | _ | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

Full index on one page (can be huge)



The default Python prompt of the interactive shell. Often seen for code examples which can be executed interactively in the interpreter.


Can refer to:

  • The default Python prompt of the interactive shell when entering the code for an indented code block, when within a pair of matching left and right delimiters (parentheses, square brackets, curly braces or triple quotes), or after specifying a decorator.

  • The Ellipsis built-in constant.


A tool that tries to convert Python 2.x code to Python 3.x code by handling most of the incompatibilities which can be detected by parsing the source and traversing the parse tree.

2to3 is available in the standard library as lib2to3; a standalone entry point is provided as Tools/scripts/2to3. See 2to3 – Automated Python 2 to 3 code translation.

abstract base class

Abstract base classes complement duck-typing by providing a way to define interfaces when other techniques like hasattr() would be clumsy or subtly wrong (for example with magic methods). ABCs introduce virtual subclasses, which are classes that don’t inherit from a class but are still recognized by isinstance() and issubclass(); see the abc module documentation. Python comes with many built-in ABCs for data structures (in the collections.abc module), numbers (in the numbers module), streams (in the io module), import finders and loaders (in the importlib.abc module). You can create your own ABCs with the abc module.


A label associated with a variable, a class attribute or a function parameter or return value, used by convention as a type hint.

Annotations of local variables cannot be accessed at runtime, but annotations of global variables, class attributes, and functions are stored in the __annotations__ special attribute of modules, classes, and functions, respectively.

See variable annotationfunction annotationPEP 484 and PEP 526, which describe this functionality. Also see Annotations Best Practices for best practices on working with annotations.


A value passed to a function (or method) when calling the function. There are two kinds of argument:

  • keyword argument: an argument preceded by an identifier (e.g. name=) in a function call or passed as a value in a dictionary preceded by **. For example, 3 and 5 are both keyword arguments in the following calls to complex():

    complex(real=3, imag=5)
    complex(**{'real': 3, 'imag': 5})
  • positional argument: an argument that is not a keyword argument. Positional arguments can appear at the beginning of an argument list and/or be passed as elements of an iterable preceded by *. For example, 3 and 5 are both positional arguments in the following calls:

    complex(3, 5)
    complex(*(3, 5))

Arguments are assigned to the named local variables in a function body. See the Calls section for the rules governing this assignment. Syntactically, any expression can be used to represent an argument; the evaluated value is assigned to the local variable.

See also the parameter glossary entry, the FAQ question on the difference between arguments and parameters, and PEP 362.

asynchronous context manager

An object which controls the environment seen in an async with statement by defining __aenter__() and __aexit__() methods. Introduced by PEP 492.

asynchronous generator

A function which returns an asynchronous generator iterator. It looks like a coroutine function defined with async def except that it contains yield expressions for producing a series of values usable in an async for loop.

Usually refers to an asynchronous generator function, but may refer to an asynchronous generator iterator in some contexts. In cases where the intended meaning isn’t clear, using the full terms avoids ambiguity.

An asynchronous generator function may contain await expressions as well as async for, and async with statements.

asynchronous generator iterator

An object created by a asynchronous generator function.

This is an asynchronous iterator which when called using the __anext__() method returns an awaitable object which will execute the body of the asynchronous generator function until the next yield expression.

Each yield temporarily suspends processing, remembering the location execution state (including local variables and pending try-statements). When the asynchronous generator iterator effectively resumes with another awaitable returned by __anext__(), it picks up where it left off. See PEP 492 and PEP 525.

asynchronous iterable

An object, that can be used in an async for statement. Must return an asynchronous iterator from its __aiter__() method. Introduced by PEP 492.

asynchronous iterator

An object that implements the __aiter__() and __anext__() methods. __anext__ must return an awaitable object. async for resolves the awaitables returned by an asynchronous iterator’s __anext__() method until it raises a StopAsyncIteration exception. Introduced by PEP 492.


A value associated with an object which is referenced by name using dotted expressions. For example, if an object o has an attribute a it would be referenced as o.a.


An object that can be used in an await expression. Can be a coroutine or an object with an __await__() method. See also PEP 492.


Benevolent Dictator For Life, a.k.a. Guido van Rossum, Python’s creator.

binary file

file object able to read and write bytes-like objects. Examples of binary files are files opened in binary mode ('rb''wb' or 'rb+'), sys.stdin.buffersys.stdout.buffer, and instances of io.BytesIO and gzip.GzipFile.

See also text file for a file object able to read and write str objects.

borrowed reference

In Python’s C API, a borrowed reference is a reference to an object. It does not modify the object reference count. It becomes a dangling pointer if the object is destroyed. For example, a garbage collection can remove the last strong reference to the object and so destroy it.

Calling Py_INCREF() on the borrowed reference is recommended to convert it to a strong reference in-place, except when the object cannot be destroyed before the last usage of the borrowed reference. The Py_NewRef() function can be used to create a new strong reference.

bytes-like object

An object that supports the Buffer Protocol and can export a C-contiguous buffer. This includes all bytesbytearray, and array.array objects, as well as many common memoryview objects. Bytes-like objects can be used for various operations that work with binary data; these include compression, saving to a binary file, and sending over a socket.

Some operations need the binary data to be mutable. The documentation often refers to these as “read-write bytes-like objects”. Example mutable buffer objects include bytearray and a memoryview of a bytearray. Other operations require the binary data to be stored in immutable objects (“read-only bytes-like objects”); examples of these include bytes and a memoryview of a bytes object.


Python source code is compiled into bytecode, the internal representation of a Python program in the CPython interpreter. The bytecode is also cached in .pyc files so that executing the same file is faster the second time (recompilation from source to bytecode can be avoided). This “intermediate language” is said to run on a virtual machine that executes the machine code corresponding to each bytecode. Do note that bytecodes are not expected to work between different Python virtual machines, nor to be stable between Python releases.

A list of bytecode instructions can be found in the documentation for the dis module.


A subroutine function which is passed as an argument to be executed at some point in the future.


A template for creating user-defined objects. Class definitions normally contain method definitions which operate on instances of the class.

class variable

A variable defined in a class and intended to be modified only at class level (i.e., not in an instance of the class).


The implicit conversion of an instance of one type to another during an operation which involves two arguments of the same type. For example, int(3.15) converts the floating point number to the integer 3, but in 3+4.5, each argument is of a different type (one int, one float), and both must be converted to the same type before they can be added or it will raise a TypeError. Without coercion, all arguments of even compatible types would have to be normalized to the same value by the programmer, e.g., float(3)+4.5 rather than just 3+4.5.

complex number

An extension of the familiar real number system in which all numbers are expressed as a sum of a real part and an imaginary part. Imaginary numbers are real multiples of the imaginary unit (the square root of -1), often written i in mathematics or j in engineering. Python has built-in support for complex numbers, which are written with this latter notation; the imaginary part is written with a j suffix, e.g., 3+1j. To get access to complex equivalents of the math module, use cmath. Use of complex numbers is a fairly advanced mathematical feature. If you’re not aware of a need for them, it’s almost certain you can safely ignore them.

context manager

An object which controls the environment seen in a with statement by defining __enter__() and __exit__() methods. See PEP 343.

context variable

A variable which can have different values depending on its context. This is similar to Thread-Local Storage in which each execution thread may have a different value for a variable. However, with context variables, there may be several contexts in one execution thread and the main usage for context variables is to keep track of variables in concurrent asynchronous tasks. See contextvars.


A buffer is considered contiguous exactly if it is either C-contiguous or Fortran contiguous. Zero-dimensional buffers are C and Fortran contiguous. In one-dimensional arrays, the items must be laid out in memory next to each other, in order of increasing indexes starting from zero. In multidimensional C-contiguous arrays, the last index varies the fastest when visiting items in order of memory address. However, in Fortran contiguous arrays, the first index varies the fastest.


Coroutines are a more generalized form of subroutines. Subroutines are entered at one point and exited at another point. Coroutines can be entered, exited, and resumed at many different points. They can be implemented with the async def statement. See also PEP 492.

coroutine function

A function which returns a coroutine object. A coroutine function may be defined with the async def statement, and may contain awaitasync for, and async with keywords. These were introduced by PEP 492.


The canonical implementation of the Python programming language, as distributed on python.org. The term “CPython” is used when necessary to distinguish this implementation from others such as Jython or IronPython.


A function returning another function, usually applied as a function transformation using the @wrapper syntax. Common examples for decorators are classmethod() and staticmethod().

The decorator syntax is merely syntactic sugar, the following two function definitions are semantically equivalent:

def f(arg):
f = staticmethod(f)

def f(arg):

The same concept exists for classes, but is less commonly used there. See the documentation for function definitions and class definitions for more about decorators.


Any object which defines the methods __get__()__set__(), or __delete__(). When a class attribute is a descriptor, its special binding behavior is triggered upon attribute lookup. Normally, using a.b to get, set or delete an attribute looks up the object named b in the class dictionary for a, but if b is a descriptor, the respective descriptor method gets called. Understanding descriptors is a key to a deep understanding of Python because they are the basis for many features including functions, methods, properties, class methods, static methods, and reference to super classes.

For more information about descriptors’ methods, see Implementing Descriptors or the Descriptor How To Guide.


An associative array, where arbitrary keys are mapped to values. The keys can be any object with __hash__() and __eq__() methods. Called a hash in Perl.

dictionary comprehension

A compact way to process all or part of the elements in an iterable and return a dictionary with the results. results = {n: n ** 2 for n in range(10)} generates a dictionary containing key n mapped to value n ** 2. See Displays for lists, sets and dictionaries.

dictionary view

The objects returned from dict.keys()dict.values(), and dict.items() are called dictionary views. They provide a dynamic view on the dictionary’s entries, which means that when the dictionary changes, the view reflects these changes. To force the dictionary view to become a full list use list(dictview). See Dictionary view objects.


A string literal which appears as the first expression in a class, function or module. While ignored when the suite is executed, it is recognized by the compiler and put into the __doc__ attribute of the enclosing class, function or module. Since it is available via introspection, it is the canonical place for documentation of the object.


A programming style which does not look at an object’s type to determine if it has the right interface; instead, the method or attribute is simply called or used (“If it looks like a duck and quacks like a duck, it must be a duck.”) By emphasizing interfaces rather than specific types, well-designed code improves its flexibility by allowing polymorphic substitution. Duck-typing avoids tests using type() or isinstance(). (Note, however, that duck-typing can be complemented with abstract base classes.) Instead, it typically employs hasattr() tests or EAFP programming.


Easier to ask for forgiveness than permission. This common Python coding style assumes the existence of valid keys or attributes and catches exceptions if the assumption proves false. This clean and fast style is characterized by the presence of many try and except statements. The technique contrasts with the LBYL style common to many other languages such as C.


A piece of syntax which can be evaluated to some value. In other words, an expression is an accumulation of expression elements like literals, names, attribute access, operators or function calls which all return a value. In contrast to many other languages, not all language constructs are expressions. There are also statements which cannot be used as expressions, such as while. Assignments are also statements, not expressions.

extension module

A module written in C or C++, using Python’s C API to interact with the core and with user code.


String literals prefixed with 'f' or 'F' are commonly called “f-strings” which is short for formatted string literals. See also PEP 498.

file object

An object exposing a file-oriented API (with methods such as read() or write()) to an underlying resource. Depending on the way it was created, a file object can mediate access to a real on-disk file or to another type of storage or communication device (for example standard input/output, in-memory buffers, sockets, pipes, etc.). File objects are also called file-like objects or streams.

There are actually three categories of file objects: raw binary files, buffered binary files and text files. Their interfaces are defined in the io module. The canonical way to create a file object is by using the open() function.

file-like object

A synonym for file object.

filesystem encoding and error handler

Encoding and error handler used by Python to decode bytes from the operating system and encode Unicode to the operating system.

The filesystem encoding must guarantee to successfully decode all bytes below 128. If the file system encoding fails to provide this guarantee, API functions can raise UnicodeError.

The sys.getfilesystemencoding() and sys.getfilesystemencodeerrors() functions can be used to get the filesystem encoding and error handler.

The filesystem encoding and error handler are configured at Python startup by the PyConfig_Read() function: see filesystem_encoding and filesystem_errors members of PyConfig.

See also the locale encoding.


An object that tries to find the loader for a module that is being imported.

Since Python 3.3, there are two types of finder: meta path finders for use with sys.meta_path, and path entry finders for use with sys.path_hooks.

See PEP 302PEP 420 and PEP 451 for much more detail.

floor division

Mathematical division that rounds down to nearest integer. The floor division operator is //. For example, the expression 11 // 4 evaluates to 2 in contrast to the 2.75 returned by float true division. Note that (-11) // 4 is -3 because that is -2.75 rounded downward. See PEP 238.


A series of statements which returns some value to a caller. It can also be passed zero or more arguments which may be used in the execution of the body. See also parametermethod, and the Function definitions section.

function annotation

An annotation of a function parameter or return value.

Function annotations are usually used for type hints: for example, this function is expected to take two int arguments and is also expected to have an int return value:

def sum_two_numbers(a: int, b: int) -> int:
   return a + b

Function annotation syntax is explained in section Function definitions.

See variable annotation and PEP 484, which describe this functionality. Also see Annotations Best Practices for best practices on working with annotations.


future statementfrom __future__ import <feature>, directs the compiler to compile the current module using syntax or semantics that will become standard in a future release of Python. The __future__ module documents the possible values of feature. By importing this module and evaluating its variables, you can see when a new feature was first added to the language and when it will (or did) become the default:

>>> import __future__
>>> __future__.division
_Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)
garbage collection

The process of freeing memory when it is not used anymore. Python performs garbage collection via reference counting and a cyclic garbage collector that is able to detect and break reference cycles. The garbage collector can be controlled using the gc module.


A function which returns a generator iterator. It looks like a normal function except that it contains yield expressions for producing a series of values usable in a for-loop or that can be retrieved one at a time with the next() function.

Usually refers to a generator function, but may refer to a generator iterator in some contexts. In cases where the intended meaning isn’t clear, using the full terms avoids ambiguity.

generator iterator

An object created by a generator function.

Each yield temporarily suspends processing, remembering the location execution state (including local variables and pending try-statements). When the generator iterator resumes, it picks up where it left off (in contrast to functions which start fresh on every invocation).

generator expression

An expression that returns an iterator. It looks like a normal expression followed by a for clause defining a loop variable, range, and an optional if clause. The combined expression generates values for an enclosing function:

>>> sum(i*i for i in range(10))         # sum of squares 0, 1, 4, ... 81
generic function

A function composed of multiple functions implementing the same operation for different types. Which implementation should be used during a call is determined by the dispatch algorithm.

See also the single dispatch glossary entry, the functools.singledispatch() decorator, and PEP 443.

generic type

type that can be parameterized; typically a container class such as list or dict. Used for type hints and annotations.

For more details, see generic alias typesPEP 483PEP 484PEP 585, and the typing module.


See global interpreter lock.

global interpreter lock

The mechanism used by the CPython interpreter to assure that only one thread executes Python bytecode at a time. This simplifies the CPython implementation by making the object model (including critical built-in types such as dict) implicitly safe against concurrent access. Locking the entire interpreter makes it easier for the interpreter to be multi-threaded, at the expense of much of the parallelism afforded by multi-processor machines.

However, some extension modules, either standard or third-party, are designed so as to release the GIL when doing computationally-intensive tasks such as compression or hashing. Also, the GIL is always released when doing I/O.

Past efforts to create a “free-threaded” interpreter (one which locks shared data at a much finer granularity) have not been successful because performance suffered in the common single-processor case. It is believed that overcoming this performance issue would make the implementation much more complicated and therefore costlier to maintain.

hash-based pyc

A bytecode cache file that uses the hash rather than the last-modified time of the corresponding source file to determine its validity. See Cached bytecode invalidation.


An object is hashable if it has a hash value which never changes during its lifetime (it needs a __hash__() method), and can be compared to other objects (it needs an __eq__() method). Hashable objects which compare equal must have the same hash value.

Hashability makes an object usable as a dictionary key and a set member, because these data structures use the hash value internally.

Most of Python’s immutable built-in objects are hashable; mutable containers (such as lists or dictionaries) are not; immutable containers (such as tuples and frozensets) are only hashable if their elements are hashable. Objects which are instances of user-defined classes are hashable by default. They all compare unequal (except with themselves), and their hash value is derived from their id().


An Integrated Development Environment for Python. IDLE is a basic editor and interpreter environment which ships with the standard distribution of Python.


An object with a fixed value. Immutable objects include numbers, strings and tuples. Such an object cannot be altered. A new object has to be created if a different value has to be stored. They play an important role in places where a constant hash value is needed, for example as a key in a dictionary.

import path

A list of locations (or path entries) that are searched by the path based finder for modules to import. During import, this list of locations usually comes from sys.path, but for subpackages it may also come from the parent package’s __path__ attribute.


The process by which Python code in one module is made available to Python code in another module.


An object that both finds and loads a module; both a finder and loader object.


Python has an interactive interpreter which means you can enter statements and expressions at the interpreter prompt, immediately execute them and see their results. Just launch python with no arguments (possibly by selecting it from your computer’s main menu). It is a very powerful way to test out new ideas or inspect modules and packages (remember help(x)).


Python is an interpreted language, as opposed to a compiled one, though the distinction can be blurry because of the presence of the bytecode compiler. This means that source files can be run directly without explicitly creating an executable which is then run. Interpreted languages typically have a shorter development/debug cycle than compiled ones, though their programs generally also run more slowly. See also interactive.

interpreter shutdown

When asked to shut down, the Python interpreter enters a special phase where it gradually releases all allocated resources, such as modules and various critical internal structures. It also makes several calls to the garbage collector. This can trigger the execution of code in user-defined destructors or weakref callbacks. Code executed during the shutdown phase can encounter various exceptions as the resources it relies on may not function anymore (common examples are library modules or the warnings machinery).

The main reason for interpreter shutdown is that the __main__ module or the script being run has finished executing.


An object capable of returning its members one at a time. Examples of iterables include all sequence types (such as liststr, and tuple) and some non-sequence types like dictfile objects, and objects of any classes you define with an __iter__() method or with a __getitem__() method that implements Sequence semantics.

Iterables can be used in a for loop and in many other places where a sequence is needed (zip()map(), …). When an iterable object is passed as an argument to the built-in function iter(), it returns an iterator for the object. This iterator is good for one pass over the set of values. When using iterables, it is usually not necessary to call iter() or deal with iterator objects yourself. The for statement does that automatically for you, creating a temporary unnamed variable to hold the iterator for the duration of the loop. See also iteratorsequence, and generator.


An object representing a stream of data. Repeated calls to the iterator’s __next__() method (or passing it to the built-in function next()) return successive items in the stream. When no more data are available a StopIteration exception is raised instead. At this point, the iterator object is exhausted and any further calls to its __next__() method just raise StopIteration again. Iterators are required to have an __iter__() method that returns the iterator object itself so every iterator is also iterable and may be used in most places where other iterables are accepted. One notable exception is code which attempts multiple iteration passes. A container object (such as a list) produces a fresh new iterator each time you pass it to the iter() function or use it in a for loop. Attempting this with an iterator will just return the same exhausted iterator object used in the previous iteration pass, making it appear like an empty container.

More information can be found in Iterator Types.

CPython implementation detail: CPython does not consistently apply the requirement that an iterator define __iter__().

key function

A key function or collation function is a callable that returns a value used for sorting or ordering. For example, locale.strxfrm() is used to produce a sort key that is aware of locale specific sort conventions.

A number of tools in Python accept key functions to control how elements are ordered or grouped. They include min()max()sorted()list.sort()heapq.merge()heapq.nsmallest()heapq.nlargest(), and itertools.groupby().

There are several ways to create a key function. For example. the str.lower() method can serve as a key function for case insensitive sorts. Alternatively, a key function can be built from a lambda expression such as lambda r: (r[0], r[2]). Also, the operator module provides three key function constructors: attrgetter()itemgetter(), and methodcaller(). See the Sorting HOW TO for examples of how to create and use key functions.

keyword argument

See argument.


An anonymous inline function consisting of a single expression which is evaluated when the function is called. The syntax to create a lambda function is lambda [parameters]: expression


Look before you leap. This coding style explicitly tests for pre-conditions before making calls or lookups. This style contrasts with the EAFP approach and is characterized by the presence of many if statements.

In a multi-threaded environment, the LBYL approach can risk introducing a race condition between “the looking” and “the leaping”. For example, the code, if key in mapping: return mapping[key] can fail if another thread removes key from mapping after the test, but before the lookup. This issue can be solved with locks or by using the EAFP approach.

locale encoding

On Unix, it is the encoding of the LC_CTYPE locale. It can be set with locale.setlocale(locale.LC_CTYPE, new_locale).

On Windows, it is the ANSI code page (ex: cp1252).

locale.getpreferredencoding(False) can be used to get the locale encoding.

Python uses the filesystem encoding and error handler to convert between Unicode filenames and bytes filenames.


A built-in Python sequence. Despite its name it is more akin to an array in other languages than to a linked list since access to elements is O(1).

list comprehension

A compact way to process all or part of the elements in a sequence and return a list with the results. result = ['{:#04x}'.format(x) for x in range(256) if x % 2 == 0] generates a list of strings containing even hex numbers (0x..) in the range from 0 to 255. The if clause is optional. If omitted, all elements in range(256) are processed.


An object that loads a module. It must define a method named load_module(). A loader is typically returned by a finder. See PEP 302 for details and importlib.abc.Loader for an abstract base class.

magic method

An informal synonym for special method.


A container object that supports arbitrary key lookups and implements the methods specified in the Mapping or MutableMapping abstract base classes. Examples include dictcollections.defaultdictcollections.OrderedDict and collections.Counter.

meta path finder

finder returned by a search of sys.meta_path. Meta path finders are related to, but different from path entry finders.

See importlib.abc.MetaPathFinder for the methods that meta path finders implement.


The class of a class. Class definitions create a class name, a class dictionary, and a list of base classes. The metaclass is responsible for taking those three arguments and creating the class. Most object oriented programming languages provide a default implementation. What makes Python special is that it is possible to create custom metaclasses. Most users never need this tool, but when the need arises, metaclasses can provide powerful, elegant solutions. They have been used for logging attribute access, adding thread-safety, tracking object creation, implementing singletons, and many other tasks.

More information can be found in Metaclasses.


A function which is defined inside a class body. If called as an attribute of an instance of that class, the method will get the instance object as its first argument (which is usually called self). See function and nested scope.

method resolution order

Method Resolution Order is the order in which base classes are searched for a member during lookup. See The Python 2.3 Method Resolution Order for details of the algorithm used by the Python interpreter since the 2.3 release.


An object that serves as an organizational unit of Python code. Modules have a namespace containing arbitrary Python objects. Modules are loaded into Python by the process of importing.

See also package.

module spec

A namespace containing the import-related information used to load a module. An instance of importlib.machinery.ModuleSpec.


See method resolution order.


Mutable objects can change their value but keep their id(). See also immutable.

named tuple

The term “named tuple” applies to any type or class that inherits from tuple and whose indexable elements are also accessible using named attributes. The type or class may have other features as well.

Several built-in types are named tuples, including the values returned by time.localtime() and os.stat(). Another example is sys.float_info:

>>> sys.float_info[1]                   # indexed access
>>> sys.float_info.max_exp              # named field access
>>> isinstance(sys.float_info, tuple)   # kind of tuple

Some named tuples are built-in types (such as the above examples). Alternatively, a named tuple can be created from a regular class definition that inherits from tuple and that defines named fields. Such a class can be written by hand or it can be created with the factory function collections.namedtuple(). The latter technique also adds some extra methods that may not be found in hand-written or built-in named tuples.


The place where a variable is stored. Namespaces are implemented as dictionaries. There are the local, global and built-in namespaces as well as nested namespaces in objects (in methods). Namespaces support modularity by preventing naming conflicts. For instance, the functions builtins.open and os.open() are distinguished by their namespaces. Namespaces also aid readability and maintainability by making it clear which module implements a function. For instance, writing random.seed() or itertools.islice() makes it clear that those functions are implemented by the random and itertools modules, respectively.

namespace package

PEP 420 package which serves only as a container for subpackages. Namespace packages may have no physical representation, and specifically are not like a regular package because they have no __init__.py file.

See also module.

nested scope

The ability to refer to a variable in an enclosing definition. For instance, a function defined inside another function can refer to variables in the outer function. Note that nested scopes by default work only for reference and not for assignment. Local variables both read and write in the innermost scope. Likewise, global variables read and write to the global namespace. The nonlocal allows writing to outer scopes.

new-style class

Old name for the flavor of classes now used for all class objects. In earlier Python versions, only new-style classes could use Python’s newer, versatile features like __slots__, descriptors, properties, __getattribute__(), class methods, and static methods.


Any data with state (attributes or value) and defined behavior (methods). Also the ultimate base class of any new-style class.


A Python module which can contain submodules or recursively, subpackages. Technically, a package is a Python module with an __path__ attribute.

See also regular package and namespace package.


A named entity in a function (or method) definition that specifies an argument (or in some cases, arguments) that the function can accept. There are five kinds of parameter:

  • positional-or-keyword: specifies an argument that can be passed either positionally or as a keyword argument. This is the default kind of parameter, for example foo and bar in the following:

    def func(foo, bar=None): ...
  • positional-only: specifies an argument that can be supplied only by position. Positional-only parameters can be defined by including a / character in the parameter list of the function definition after them, for example posonly1 and posonly2 in the following:

    def func(posonly1, posonly2, /, positional_or_keyword): ...
  • keyword-only: specifies an argument that can be supplied only by keyword. Keyword-only parameters can be defined by including a single var-positional parameter or bare * in the parameter list of the function definition before them, for example kw_only1 and kw_only2 in the following:

    def func(arg, *, kw_only1, kw_only2): ...
  • var-positional: specifies that an arbitrary sequence of positional arguments can be provided (in addition to any positional arguments already accepted by other parameters). Such a parameter can be defined by prepending the parameter name with *, for example args in the following:

    def func(*args, **kwargs): ...
  • var-keyword: specifies that arbitrarily many keyword arguments can be provided (in addition to any keyword arguments already accepted by other parameters). Such a parameter can be defined by prepending the parameter name with **, for example kwargs in the example above.

Parameters can specify both optional and required arguments, as well as default values for some optional arguments.

See also the argument glossary entry, the FAQ question on the difference between arguments and parameters, the inspect.Parameter class, the Function definitions section, and PEP 362.

path entry

A single location on the import path which the path based finder consults to find modules for importing.

path entry finder

finder returned by a callable on sys.path_hooks (i.e. a path entry hook) which knows how to locate modules given a path entry.

See importlib.abc.PathEntryFinder for the methods that path entry finders implement.

path entry hook

A callable on the sys.path_hook list which returns a path entry finder if it knows how to find modules on a specific path entry.

path based finder

One of the default meta path finders which searches an import path for modules.

path-like object

An object representing a file system path. A path-like object is either a str or bytes object representing a path, or an object implementing the os.PathLike protocol. An object that supports the os.PathLike protocol can be converted to a str or bytes file system path by calling the os.fspath() function; os.fsdecode() and os.fsencode() can be used to guarantee a str or bytes result instead, respectively. Introduced by PEP 519.


Python Enhancement Proposal. A PEP is a design document providing information to the Python community, or describing a new feature for Python or its processes or environment. PEPs should provide a concise technical specification and a rationale for proposed features.

PEPs are intended to be the primary mechanisms for proposing major new features, for collecting community input on an issue, and for documenting the design decisions that have gone into Python. The PEP author is responsible for building consensus within the community and documenting dissenting opinions.

See PEP 1.


A set of files in a single directory (possibly stored in a zip file) that contribute to a namespace package, as defined in PEP 420.

positional argument

See argument.

provisional API

A provisional API is one which has been deliberately excluded from the standard library’s backwards compatibility guarantees. While major changes to such interfaces are not expected, as long as they are marked provisional, backwards incompatible changes (up to and including removal of the interface) may occur if deemed necessary by core developers. Such changes will not be made gratuitously – they will occur only if serious fundamental flaws are uncovered that were missed prior to the inclusion of the API.

Even for provisional APIs, backwards incompatible changes are seen as a “solution of last resort” – every attempt will still be made to find a backwards compatible resolution to any identified problems.

This process allows the standard library to continue to evolve over time, without locking in problematic design errors for extended periods of time. See PEP 411 for more details.

provisional package

See provisional API.

Python 3000

Nickname for the Python 3.x release line (coined long ago when the release of version 3 was something in the distant future.) This is also abbreviated “Py3k”.


An idea or piece of code which closely follows the most common idioms of the Python language, rather than implementing code using concepts common to other languages. For example, a common idiom in Python is to loop over all elements of an iterable using a for statement. Many other languages don’t have this type of construct, so people unfamiliar with Python sometimes use a numerical counter instead:

for i in range(len(food)):

As opposed to the cleaner, Pythonic method:

for piece in food:
qualified name

A dotted name showing the “path” from a module’s global scope to a class, function or method defined in that module, as defined in PEP 3155. For top-level functions and classes, the qualified name is the same as the object’s name:

>>> class C:
...     class D:
...         def meth(self):
...             pass
>>> C.__qualname__
>>> C.D.__qualname__
>>> C.D.meth.__qualname__

When used to refer to modules, the fully qualified name means the entire dotted path to the module, including any parent packages, e.g. email.mime.text:

>>> import email.mime.text
>>> email.mime.text.__name__
reference count

The number of references to an object. When the reference count of an object drops to zero, it is deallocated. Reference counting is generally not visible to Python code, but it is a key element of the CPython implementation. The sys module defines a getrefcount() function that programmers can call to return the reference count for a particular object.

regular package

A traditional package, such as a directory containing an __init__.py file.

See also namespace package.


A declaration inside a class that saves memory by pre-declaring space for instance attributes and eliminating instance dictionaries. Though popular, the technique is somewhat tricky to get right and is best reserved for rare cases where there are large numbers of instances in a memory-critical application.


An iterable which supports efficient element access using integer indices via the __getitem__() special method and defines a __len__() method that returns the length of the sequence. Some built-in sequence types are liststrtuple, and bytes. Note that dict also supports __getitem__() and __len__(), but is considered a mapping rather than a sequence because the lookups use arbitrary immutable keys rather than integers.

The collections.abc.Sequence abstract base class defines a much richer interface that goes beyond just __getitem__() and __len__(), adding count()index()__contains__(), and __reversed__(). Types that implement this expanded interface can be registered explicitly using register().

set comprehension

A compact way to process all or part of the elements in an iterable and return a set with the results. results = {c for c in 'abracadabra' if c not in 'abc'} generates the set of strings {'r', 'd'}. See Displays for lists, sets and dictionaries.

single dispatch

A form of generic function dispatch where the implementation is chosen based on the type of a single argument.


An object usually containing a portion of a sequence. A slice is created using the subscript notation, [] with colons between numbers when several are given, such as in variable_name[1:3:5]. The bracket (subscript) notation uses slice objects internally.

special method

A method that is called implicitly by Python to execute a certain operation on a type, such as addition. Such methods have names starting and ending with double underscores. Special methods are documented in Special method names.


A statement is part of a suite (a “block” of code). A statement is either an expression or one of several constructs with a keyword, such as ifwhile or for.

strong reference

In Python’s C API, a strong reference is a reference to an object which increments the object’s reference count when it is created and decrements the object’s reference count when it is deleted.

The Py_NewRef() function can be used to create a strong reference to an object. Usually, the Py_DECREF() function must be called on the strong reference before exiting the scope of the strong reference, to avoid leaking one reference.

See also borrowed reference.

text encoding

A string in Python is a sequence of Unicode code points (in range U+0000U+10FFFF). To store or transfer a string, it needs to be serialized as a sequence of bytes.

Serializing a string into a sequence of bytes is known as “encoding”, and recreating the string from the sequence of bytes is known as “decoding”.

There are a variety of different text serialization codecs, which are collectively referred to as “text encodings”.

text file

file object able to read and write str objects. Often, a text file actually accesses a byte-oriented datastream and handles the text encoding automatically. Examples of text files are files opened in text mode ('r' or 'w'), sys.stdinsys.stdout, and instances of io.StringIO.

See also binary file for a file object able to read and write bytes-like objects.

triple-quoted string

A string which is bound by three instances of either a quotation mark (”) or an apostrophe (‘). While they don’t provide any functionality not available with single-quoted strings, they are useful for a number of reasons. They allow you to include unescaped single and double quotes within a string and they can span multiple lines without the use of the continuation character, making them especially useful when writing docstrings.


The type of a Python object determines what kind of object it is; every object has a type. An object’s type is accessible as its __class__ attribute or can be retrieved with type(obj).

type alias

A synonym for a type, created by assigning the type to an identifier.

Type aliases are useful for simplifying type hints. For example:

def remove_gray_shades(
        colors: list[tuple[int, int, int]]) -> list[tuple[int, int, int]]:

could be made more readable like this:

Color = tuple[int, int, int]

def remove_gray_shades(colors: list[Color]) -> list[Color]:

See typing and PEP 484, which describe this functionality.

type hint

An annotation that specifies the expected type for a variable, a class attribute, or a function parameter or return value.

Type hints are optional and are not enforced by Python but they are useful to static type analysis tools, and aid IDEs with code completion and refactoring.

Type hints of global variables, class attributes, and functions, but not local variables, can be accessed using typing.get_type_hints().

See typing and PEP 484, which describe this functionality.

universal newlines

A manner of interpreting text streams in which all of the following are recognized as ending a line: the Unix end-of-line convention '\n', the Windows convention '\r\n', and the old Macintosh convention '\r'. See PEP 278 and PEP 3116, as well as bytes.splitlines() for an additional use.

variable annotation

An annotation of a variable or a class attribute.

When annotating a variable or a class attribute, assignment is optional:

class C:
    field: 'annotation'

Variable annotations are usually used for type hints: for example this variable is expected to take int values:

count: int = 0

Variable annotation syntax is explained in section Annotated assignment statements.

See function annotationPEP 484 and PEP 526, which describe this functionality. Also see Annotations Best Practices for best practices on working with annotations.

virtual environment

A cooperatively isolated runtime environment that allows Python users and applications to install and upgrade Python distribution packages without interfering with the behaviour of other Python applications running on the same system.

See also venv.

virtual machine

A computer defined entirely in software. Python’s virtual machine executes the bytecode emitted by the bytecode compiler.

Zen of Python

Listing of Python design principles and philosophies that are helpful in understanding and using the language. The listing can be found by typing “import this” at the interactive prompt.

Dealing with Bugs

Python is a mature programming language which has established a reputation for stability. In order to maintain this reputation, the developers would like to know of any deficiencies you find in Python.

It can be sometimes faster to fix bugs yourself and contribute patches to Python as it streamlines the process and involves less people. Learn how to contribute.

Documentation bugs

If you find a bug in this documentation or would like to propose an improvement, please submit a bug report on the tracker. If you have a suggestion on how to fix it, include that as well.

If you’re short on time, you can also email documentation bug reports to [email protected] (behavioral bugs can be sent to [email protected]). ‘[email protected]’ is a mailing list run by volunteers; your request will be noticed, though it may take a while to be processed.

See also

Documentation bugs

A list of documentation bugs that have been submitted to the Python issue tracker.

Issue Tracking

Overview of the process involved in reporting an improvement on the tracker.

Helping with Documentation

Comprehensive guide for individuals that are interested in contributing to Python documentation.

Documentation Translations

A list of GitHub pages for documentation translation and their primary contacts.

Using the Python issue tracker

Bug reports for Python itself should be submitted via the Python Bug Tracker (https://bugs.python.org/). The bug tracker offers a web form which allows pertinent information to be entered and submitted to the developers.

The first step in filing a report is to determine whether the problem has already been reported. The advantage in doing so, aside from saving the developers time, is that you learn what has been done to fix it; it may be that the problem has already been fixed for the next release, or additional information is needed (in which case you are welcome to provide it if you can!). To do this, search the bug database using the search box on the top of the page.

If the problem you’re reporting is not already in the bug tracker, go back to the Python Bug Tracker and log in. If you don’t already have a tracker account, select the “Register” link or, if you use OpenID, one of the OpenID provider logos in the sidebar. It is not possible to submit a bug report anonymously.

Being now logged in, you can submit a bug. Select the “Create New” link in the sidebar to open the bug reporting form.

The submission form has a number of fields. For the “Title” field, enter a very short description of the problem; less than ten words is good. In the “Type” field, select the type of your problem; also select the “Component” and “Versions” to which the bug relates.

In the “Comment” field, describe the problem in detail, including what you expected to happen and what did happen. Be sure to include whether any extension modules were involved, and what hardware and software platform you were using (including version information as appropriate).

Each bug report will be assigned to a developer who will determine what needs to be done to correct the problem. You will receive an update each time action is taken on the bug.

See also

How to Report Bugs Effectively

Article which goes into some detail about how to create a useful bug report. This describes what kind of information is useful and why it is useful.

Bug Writing Guidelines

Information about writing a good bug report. Some of this is specific to the Mozilla project, but describes general good practices.

Getting started contributing to Python yourself

Beyond just reporting bugs that you find, you are also welcome to submit patches to fix them. You can find more information on how to get started patching Python in the Python Developer’s Guide. If you have questions, the core-mentorship mailing list is a friendly place to get answers to any and all questions pertaining to the process of fixing issues in Python.

From W3s