The Python Tutorial¶
Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.
The Python interpreter and the extensive standard library are freely available in source or binary form for all major platforms from the Python web site, https://www.python.org/, and may be freely distributed. The same site also contains distributions of and pointers to many free third party Python modules, programs and tools, and additional documentation.
The Python interpreter is easily extended with new functions and data types implemented in C or C++ (or other languages callable from C). Python is also suitable as an extension language for customizable applications.
This tutorial introduces the reader informally to the basic concepts and features of the Python language and system. It helps to have a Python interpreter handy for hands-on experience, but all examples are self-contained, so the tutorial can be read off-line as well.
For a description of standard objects and modules, see The Python Standard Library . The Python Language Reference gives a more formal definition of the language. To write extensions in C or C++, read Extending and Embedding the Python Interpreter and Python/C API Reference Manual . There are also several books covering Python in depth.
This tutorial does not attempt to be comprehensive and cover every single feature, or even every commonly used feature. Instead, it introduces many of Python’s most noteworthy features, and will give you a good idea of the language’s flavor and style. After reading it, you will be able to read and write Python modules and programs, and you will be ready to learn more about the various Python library modules described in The Python Standard Library .
The Glossary is also worth going through.
- 1. Whetting Your Appetite
- 2. Using the Python Interpreter
- 2.1. Invoking the Interpreter
- 2.1.1. Argument Passing
- 2.1.2. Interactive Mode
- 2.2.1. Source Code Encoding
- 3.1. Using Python as a Calculator
- 3.1.1. Numbers
- 3.1.2. Strings
- 3.1.3. Lists
- 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
- 4.8.3.1. Positional-or-Keyword Arguments
- 4.8.3.2. Positional-Only Parameters
- 4.8.3.3. Keyword-Only Arguments
- 4.8.3.4. Function Examples
- 4.8.3.5. Recap
- 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
- 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
- 6.4.1. Importing * From a Package
- 6.4.2. Intra-package References
- 6.4.3. Packages in Multiple Directories
- 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
- 7.2.1. Methods of File Objects
- 7.2.2. Saving structured data with json
- 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
- 8.9. Raising and Handling Multiple Unrelated Exceptions
- 8.10. Enriching Exceptions with Notes
- 9.1. A Word About Names and Objects
- 9.2. Python Scopes and Namespaces
- 9.2.1. Scopes and Namespaces Example
- 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
- 9.5.1. Multiple Inheritance
- 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
- 11.1. 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
- 12.1. Introduction
- 12.2. Creating Virtual Environments
- 12.3. Managing Packages with pip
- 14.1. Tab Completion and History Editing
- 14.2. Alternatives to the Interactive Interpreter
- 15.1. Representation Error
- 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
1. Introduction¶
This reference manual describes the Python programming language. It is not intended as a tutorial.
While I am trying to be as precise as possible, I chose to use English rather than formal specifications for everything except syntax and lexical analysis. This should make the document more understandable to the average reader, but will leave room for ambiguities. Consequently, if you were coming from Mars and tried to re-implement Python from this document alone, you might have to guess things and in fact you would probably end up implementing quite a different language. On the other hand, if you are using Python and wonder what the precise rules about a particular area of the language are, you should definitely be able to find them here. If you would like to see a more formal definition of the language, maybe you could volunteer your time — or invent a cloning machine :-).
It is dangerous to add too many implementation details to a language reference document — the implementation may change, and other implementations of the same language may work differently. On the other hand, CPython is the one Python implementation in widespread use (although alternate implementations continue to gain support), and its particular quirks are sometimes worth being mentioned, especially where the implementation imposes additional limitations. Therefore, you’ll find short “implementation notes” sprinkled throughout the text.
Every Python implementation comes with a number of built-in and standard modules. These are documented in The Python Standard Library . A few built-in modules are mentioned when they interact in a significant way with the language definition.
1.1. Alternate Implementations¶
Though there is one Python implementation which is by far the most popular, there are some alternate implementations which are of particular interest to different audiences.
Known implementations include:
This is the original and most-maintained implementation of Python, written in C. New language features generally appear here first.
Python implemented in Java. This implementation can be used as a scripting language for Java applications, or can be used to create applications using the Java class libraries. It is also often used to create tests for Java libraries. More information can be found at the Jython website.
This implementation actually uses the CPython implementation, but is a managed .NET application and makes .NET libraries available. It was created by Brian Lloyd. For more information, see the Python for .NET home page.
An alternate Python for .NET. Unlike Python.NET, this is a complete Python implementation that generates IL, and compiles Python code directly to .NET assemblies. It was created by Jim Hugunin, the original creator of Jython. For more information, see the IronPython website.
An implementation of Python written completely in Python. It supports several advanced features not found in other implementations like stackless support and a Just in Time compiler. One of the goals of the project is to encourage experimentation with the language itself by making it easier to modify the interpreter (since it is written in Python). Additional information is available on the PyPy project’s home page.
Each of these implementations varies in some way from the language as documented in this manual, or introduces specific information beyond what’s covered in the standard Python documentation. Please refer to the implementation-specific documentation to determine what else you need to know about the specific implementation you’re using.
1.2. Notation¶
The descriptions of lexical analysis and syntax use a modified BNF grammar notation. This uses the following style of definition:
name ::=
lc_letter(lc_letter| "_")* lc_letter ::= "a". "z"The first line says that a name is an lc_letter followed by a sequence of zero or more lc_letter s and underscores. An lc_letter in turn is any of the single characters ‘a’ through ‘z’ . (This rule is actually adhered to for the names defined in lexical and grammar rules in this document.)
Each rule begins with a name (which is the name defined by the rule) and ::= . A vertical bar ( | ) is used to separate alternatives; it is the least binding operator in this notation. A star ( * ) means zero or more repetitions of the preceding item; likewise, a plus ( + ) means one or more repetitions, and a phrase enclosed in square brackets ( [ ] ) means zero or one occurrences (in other words, the enclosed phrase is optional). The * and + operators bind as tightly as possible; parentheses are used for grouping. Literal strings are enclosed in quotes. White space is only meaningful to separate tokens. Rules are normally contained on a single line; rules with many alternatives may be formatted alternatively with each line after the first beginning with a vertical bar.
In lexical definitions (as the example above), two more conventions are used: Two literal characters separated by three dots mean a choice of any single character in the given (inclusive) range of ASCII characters. A phrase between angular brackets ( <. >) gives an informal description of the symbol defined; e.g., this could be used to describe the notion of ‘control character’ if needed.
Even though the notation used is almost the same, there is a big difference between the meaning of lexical and syntactic definitions: a lexical definition operates on the individual characters of the input source, while a syntax definition operates on the stream of tokens generated by the lexical analysis. All uses of BNF in the next chapter (“Lexical Analysis”) are lexical definitions; uses in subsequent chapters are syntactic definitions.
- 2.1. Invoking the Interpreter