Functional programming & Python!
We have two good resources on this topic.
The following is from https://docs.python.org/2/howto/functional.html.
Functional programming decomposes a problem into a set of functions. Ideally, functions only take inputs and produce outputs, and don't have any internal state that affects the output produced for a given input. Well-known functional languages include the ML family (Standard ML, OCaml, and other variants) and Haskell.
In a functional program, input flows through a set of functions. Each function operates on its input and produces some output. Functional style discourages functions with side effects that modify internal state or make other changes that aren't visible in the function's return value. Functions that have no side effects at all are called purely functional (pure function). Avoiding side effects means not using data structures that get updated as a program runs; every function's output must only depend on its input.
Some languages are very strict about purity and don't even have assignment statements such as a=3 or c = a + b, but it's difficult to avoid all side effects. Printing to the screen or writing to a disk file are side effects, for example. For example, in Python a print statement or a time.sleep(1) both return no useful value; they're only called for their side effects of sending some text to the screen or pausing execution for a second.
Python programs written in functional style usually won't go to the extreme of avoiding all I/O or all assignments; instead, they'll provide a functional-appearing interface but will use non-functional features internally. For example, the implementation of a function will still use assignments to local variables, but won't modify global variables or have other side effects.
Functional programming can be considered the opposite of object-oriented programming. Objects are little capsules containing some internal state along with a collection of method calls that let you modify this state, and programs consist of making the right set of state changes. Functional programming wants to avoid state changes as much as possible and works with data flowing between functions. In Python you might combine the two approaches by writing functions that take and return instances representing objects in your application (e-mail messages, transactions, etc.).
Functional design may seem like an odd constraint to work under. Why should you avoid objects and side effects? There are theoretical and practical advantages to the functional style:
- Formal provability
- Ease of debugging and testing
The following is from our second resource, Charming Python: Functional programming in Python:
The functional programming has at least several of the following characteristics:
- Functions are first class (objects). That is, everything you can do with "data" can be done with functions themselves (such as passing a function to another function).
- Recursion is used as a primary control structure. In some languages, no other "loop" construct exists.
- There is a focus on LISt Processing (for example, the name Lisp). Lists are often used with recursion on sub-lists as a substitute for loops.
- "Pure" functional languages eschew side-effects. This excludes the almost ubiquitous pattern in imperative languages of assigning first one, then another value to the same variable to track the program state.
- FP either discourages or outright disallows statements, and instead works with the evaluation of expressions (in other words, functions plus arguments). In the pure case, one program is one expression (plus supporting definitions).
- FP worries about what is to be computed rather than how it is to be computed.
- Much FP utilizes higher order functions (in other words, functions that operate on functions that operate on functions).
Running Python Programs (os, sys, import)
Modules and IDLE (Import, Reload, exec)
Object Types - Numbers, Strings, and None
Strings - Escape Sequence, Raw String, and Slicing
Strings - Methods
Formatting Strings - expressions and method calls
Files and os.path
Traversing directories recursively
Regular Expressions with Python
Object Types - Lists
Object Types - Dictionaries and Tuples
Functions def, *args, **kargs
map, filter, and reduce
Sets (union/intersection) and itertools - Jaccard coefficient and shingling to check plagiarism
Hashing (Hash tables and hashlib)
Dictionary Comprehension with zip
The yield keyword
Generator Functions and Expressions
Classes and Instances (__init__, __call__, etc.)
if__name__ == '__main__'
@static method vs class method
Private attributes and private methods
bits, bytes, bitstring, and constBitStream
json.dump(s) and json.load(s)
Python Object Serialization - pickle and json
Python Object Serialization - yaml and json
Priority queue and heap queue data structure
Graph data structure
Dijkstra's shortest path algorithm
Prim's spanning tree algorithm
Functional programming in Python
Remote running a local file using ssh
SQLite 3 - A. Connecting to DB, create/drop table, and insert data into a table
SQLite 3 - B. Selecting, updating and deleting data
MongoDB with PyMongo I - Installing MongoDB ...
Python HTTP Web Services - urllib, httplib2
Web scraping with Selenium for checking domain availability
REST API : Http Requests for Humans with Flask
Blog app with Tornado
Python Network Programming I - Basic Server / Client : A Basics
Python Network Programming I - Basic Server / Client : B File Transfer
Python Network Programming II - Chat Server / Client
Python Network Programming III - Echo Server using socketserver network framework
Python Network Programming IV - Asynchronous Request Handling : ThreadingMixIn and ForkingMixIn
Python Interview Questions I
Python Interview Questions II
Python Interview Questions III
Python Interview Questions IV
Python Interview Questions V
Image processing with Python image library Pillow
Python and C++ with SIP
PyDev with Eclipse
Redis with Python
NumPy array basics A
NumPy Matrix and Linear Algebra
Pandas with NumPy and Matplotlib
Batch gradient descent algorithm
Longest Common Substring Algorithm
Python Unit Test - TDD using unittest.TestCase class
Simple tool - Google page ranking by keywords
Google App Hello World
Google App webapp2 and WSGI
Uploading Google App Hello World
Python 2 vs Python 3
virtualenv and virtualenvwrapper
Uploading a big file to AWS S3 using boto module
Scheduled stopping and starting an AWS instance
Cloudera CDH5 - Scheduled stopping and starting services
Removing Cloud Files - Rackspace API with curl and subprocess
Checking if a process is running/hanging and stop/run a scheduled task on Windows
Apache Spark 1.3 with PySpark (Spark Python API) Shell
Apache Spark 1.2 Streaming
bottle 0.12.7 - Fast and simple WSGI-micro framework for small web-applications ...
Flask app with Apache WSGI on Ubuntu14/CentOS7 ...
Fabric - streamlining the use of SSH for application deployment
Ansible Quick Preview - Setting up web servers with Nginx, configure enviroments, and deploy an App
Neural Networks with backpropagation for XOR using one hidden layer
NLP - NLTK (Natural Language Toolkit) ...
RabbitMQ(Message broker server) and Celery(Task queue) ...
OpenCV3 and Matplotlib ...
Simple tool - Concatenating slides using FFmpeg ...
iPython - Signal Processing with NumPy
iPython and Jupyter - Install Jupyter, iPython Notebook, drawing with Matplotlib, and publishing it to Github
iPython and Jupyter Notebook with Embedded D3.js
Downloading YouTube videos using youtube-dl embedded with Python
Machine Learning : scikit-learn ...
Django 1.6/1.8 Web Framework ...
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