Python Language
- Getting started with Python Language
- Awesome Book
- Awesome Community
- Awesome Course
- Awesome Tutorial
- Awesome YouTube
- *args and **kwargs
- 2to3 tool
- Abstract Base Classes (abc)
- Abstract syntax tree
- Accessing Python source code and bytecode
- Alternatives to switch statement from other languages
- ArcPy
- Arrays
- Asyncio Module
- Attribute Access
- Audio
- Basic Curses with Python
- Basic Input and Output
- Binary Data
- Bitwise Operators
- Boolean Operators
- Call Python from C#
- Checking Path Existence and Permissions
- ChemPy - python package
- Classes
- CLI subcommands with precise help output
- Code blocks, execution frames, and namespaces
- Collections module
- Comments and Documentation
- Common Pitfalls
- Commonwealth Exceptions
- Comparisons
- Complex math
- Conditionals
- configparser
- Connecting Python to SQL Server
- Context Managers (“with” Statement)
- Copying data
- Counting
- Create virtual environment with virtualenvwrapper in windows
- Creating a Windows service using Python
- Creating Python packages
- ctypes
- Data Serialization
- Data Visualization with Python
- Database Access
- Date and Time
- Date Formatting
- Debugging
- Decorators
- Defining functions with list arguments
- Deployment
- Deque Module
- Descriptor
- Design Patterns
- Dictionary
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- Distribution
- Django
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- Enum
- Exceptions
- Exponentiation
- Files & Folders I/O
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- Functional Programming in Python
- Functions
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- Garbage Collection
- Generators
- getting start with GZip
- graph-tool
- groupby()
- hashlib
- Heapq
- Hidden Features
- HTML Parsing
- Idioms
- ijson
- Immutable datatypes(int, float, str, tuple and frozensets)
- Importing modules
- Incompatibilities moving from Python 2 to Python 3
- Indentation
- Indexing and Slicing
- Input, Subset and Output External Data Files using Pandas
- Introduction to RabbitMQ using AMQPStorm
- IoT Programming with Python and Raspberry PI
- Iterables and Iterators
- Itertools Module
- JSON Module
- kivy - Cross-platform Python Framework for NUI Development
- Linked List Node
- Linked lists
- List
- List comprehensions
- List Comprehensions
- Conditional List Comprehensions
- Iterate two or more list simultaneously within list comprehension
- List Comprehensions with Nested Loops
- Nested List Comprehensions
- Refactoring filter and map to list comprehensions
- List destructuring (aka packing and unpacking)
- List slicing (selecting parts of lists)
- Logging
- Loops
- Manipulating XML
- Map Function
- Math Module
- Metaclasses
- Method Overriding
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- Multidimensional arrays
- Multiprocessing
- Multithreading
- Mutable vs Immutable (and Hashable) in Python
- Neo4j and Cypher using Py2Neo
- Non-official Python implementations
- Operator module
- Operator Precedence
- Optical Character Recognition
- os.path
- Overloading
- Pandas Transform: Preform operations on groups and concatenate the results
- Parallel computation
- Parsing Command Line arguments
- Partial functions
- Performance optimization
- Pickle data serialisation
- Pillow
- pip: PyPI Package Manager
- Plotting with Matplotlib
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- Polymorphism
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- Python and Excel
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- Python concurrency
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- Set
- setup.py
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- Simple Mathematical Operators
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- Sqlite3 Module
- Stack
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- String representations of class instances: __str__ and __repr__ methods
- Subprocess Library
- sys
- tempfile NamedTemporaryFile
- Templates in python
- The __name__ special variable
- The base64 Module
- The dis module
- The Interpreter (Command Line Console)
- The locale Module
- The os Module
- The pass statement
- The Print Function
- tkinter
- Tuple
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- Unicode and bytes
- Unit Testing
- Unzipping Files
- urllib
- Usage of "pip" module: PyPI Package Manager
- User-Defined Methods
- Using loops within functions
- Variable Scope and Binding
- virtual environment with virtualenvwrapper
- Virtual environments
- Web scraping with Python
- Web Server Gateway Interface (WSGI)
- Webbrowser Module
- Websockets
- Working around the Global Interpreter Lock (GIL)
- Working with ZIP archives
- Writing extensions
- Writing to CSV from String or List
Python Language List Comprehensions
Introduction
A list comprehension is a syntactical tool for creating lists in a natural and concise way, as illustrated in the following code to make a list of squares of the numbers 1 to 10:
[i ** 2 for i in range(1,11)] The dummy i from an existing list range is used to make a new element pattern. It is used where a for loop would be necessary in less expressive languages.
Syntax
- [i for i in range(10)] # basic list comprehension
- [i for i in xrange(10)] # basic list comprehension with generator object in python 2.x
- [i for i in range(20) if i % 2 == 0] # with filter
- [x + y for x in [1, 2, 3] for y in [3, 4, 5]] # nested loops
- [i if i > 6 else 0 for i in range(10)] # ternary expression
- [i if i > 4 else 0 for i in range(20) if i % 2 == 0] # with filter and ternary expression
- [[x + y for x in [1, 2, 3]] for y in [3, 4, 5]] # nested list comprehension
Remarks
List comprehensions were outlined in PEP 202 and introduced in Python 2.0.
List Comprehensions Related Examples
- Conditional List Comprehensions
- Iterate two or more list simultaneously within list comprehension
- List Comprehensions with Nested Loops
- Nested List Comprehensions
- Refactoring filter and map to list comprehensions
