Functions Defined
The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. More about defining functions in Python 3
# Python 3: List comprehensions >>> fruits = ['Banana', 'Apple', 'Lime'] >>> loud_fruits = [fruit.upper() for fruit in fruits] >>> print(loud_fruits) ['BANANA', 'APPLE', 'LIME'] # List and the enumerate function >>> list(enumerate(fruits)) [(0, 'Banana'), (1, 'Apple'), (2, 'Lime')]Compound Data Types
Lists (known as arrays in other languages) are one of the compound data types that Python understands. Lists can be indexed, sliced and manipulated with other built-in functions. More about lists in Python 3
# Python 3: Simple arithmetic >>> 1 / 2 0.5 >>> 2 ** 3 8 >>> 17 / 3 # classic division returns a float 5.666666666666667 >>> 17 // 3 # floor division 5Intuitive Interpretation
Calculations are simple with Python, and expression syntax is straightforward: the operators + , — , * and / work as expected; parentheses () can be used for grouping. More about simple math functions in Python 3.
# For loop on a list >>> numbers = [2, 4, 6, 8] >>> product = 1 >>> for number in numbers: . product = product * number . >>> print('The product is:', product) The product is: 384All the Flow You’d Expect
Python knows the usual control flow statements that other languages speak — if , for , while and range — with some of its own twists, of course. More control flow tools in Python 3
# Simple output (with Unicode) >>> print("Hello, I'm Python!") Hello, I'm Python! # Input, assignment >>> name = input('What is your name?\n') What is your name? Python >>> print(f'Hi, .') Hi, Python. Quick & Easy to Learn
Experienced programmers in any other language can pick up Python very quickly, and beginners find the clean syntax and indentation structure easy to learn. Whet your appetite with our Python 3 overview.
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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. Text
- 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
- 2.1. Invoking the Interpreter