# SuperFastPython.com
# example of logging from multiple workers in the multiprocessing pool
from random import random
from time import sleep
from multiprocessing import current_process
from multiprocessing import Pool
from multiprocessing import Queue
from multiprocessing import Manager
from logging.handlers import QueueHandler
import logging
 
# executed in a process that performs logging
def logger_process(queue):
    # create a logger
    logger = logging.getLogger('app')
    # configure a stream handler
    logger.addHandler(logging.StreamHandler())
    # log all messages, debug and up
    logger.setLevel(logging.DEBUG)
    # report that the logger process is running
    logger.info(f'Logger process running.')
    # run forever
    while True:
        # consume a log message, block until one arrives
        message = queue.get()
        # check for shutdown
        if message is None:
            logger.info(f'Logger process shutting down.')
            break
        # log the message
        logger.handle(message)
 
# task to be executed in child processes
def task(queue):
    # create a logger
    logger = logging.getLogger('app')
    # add a handler that uses the shared queue
    logger.addHandler(QueueHandler(queue))
    # log all messages, debug and up
    logger.setLevel(logging.DEBUG)
    # get the current process
    process = current_process()
    # report initial message
    logger.info(f'Child {process.name} starting.')
    # simulate doing work
    for i in range(5):
        # report a message
        logger.debug(f'Child {process.name} step {i}.')
        # block
        sleep(random())
    # report final message
    logger.info(f'Child {process.name} done.')
 
# protect the entry point
if __name__ == '__main__':
    # create the manager
    with Manager() as manager:
        # create the shared queue and get the proxy object
        queue = manager.Queue()
        # create a logger
        logger = logging.getLogger('app')
        # add a handler that uses the shared queue
        logger.addHandler(QueueHandler(queue))
        # log all messages, debug and up
        logger.setLevel(logging.DEBUG)
        # create the process pool with default configuration
        with Pool() as pool:
            # issue a long running task to receive logging messages
            _ = pool.apply_async(logger_process, args=(queue,))
            # report initial message
            logger.info('Main process started.')
            # issue task to the process pool
            results = [pool.apply_async(task, args=(queue,)) for i in range(5)]
            # wait for all issued tasks to complete
            logger.info('Main process waiting...')
            for result in results:
                result.wait()
            # report final message
            logger.info('Main process done.')
            # shutdown the long running logger task
            queue.put(None)
            # close the process pool
            pool.close()
            # wait for all tasks to complete (e.g. the logger to close)
            pool.join() 

Python Online Compiler

Write, Run & Share Python code online using OneCompiler's Python online compiler for free. It's one of the robust, feature-rich online compilers for python language, supporting both the versions which are Python 3 and Python 2.7. Getting started with the OneCompiler's Python editor is easy and fast. The editor shows sample boilerplate code when you choose language as Python or Python2 and start coding.

Taking inputs (stdin)

OneCompiler's python online editor supports stdin and users can give inputs to programs using the STDIN textbox under the I/O tab. Following is a sample python program which takes name as input and print your name with hello.

import sys
name = sys.stdin.readline()
print("Hello "+ name)

About Python

Python is a very popular general-purpose programming language which was created by Guido van Rossum, and released in 1991. It is very popular for web development and you can build almost anything like mobile apps, web apps, tools, data analytics, machine learning etc. It is designed to be simple and easy like english language. It's is highly productive and efficient making it a very popular language.

Tutorial & Syntax help

Loops

1. If-Else:

When ever you want to perform a set of operations based on a condition IF-ELSE is used.

if conditional-expression
    #code
elif conditional-expression
    #code
else:
    #code

Note:

Indentation is very important in Python, make sure the indentation is followed correctly

2. For:

For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.

Example:

mylist=("Iphone","Pixel","Samsung")
for i in mylist:
    print(i)

3. While:

While is also used to iterate a set of statements based on a condition. Usually while is preferred when number of iterations are not known in advance.

while condition  
    #code 

Collections

There are four types of collections in Python.

1. List:

List is a collection which is ordered and can be changed. Lists are specified in square brackets.

Example:

mylist=["iPhone","Pixel","Samsung"]
print(mylist)

2. Tuple:

Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.

Example:

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)

Below throws an error if you assign another value to tuple again.

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
myTuple[1]="onePlus"
print(myTuple)

3. Set:

Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.

Example:

myset = {"iPhone","Pixel","Samsung"}
print(myset)

4. Dictionary:

Dictionary is a collection of key value pairs which is unordered, can be changed, and indexed. They are written in curly brackets with key - value pairs.

Example:

mydict = {
    "brand" :"iPhone",
    "model": "iPhone 11"
}
print(mydict)

Supported Libraries

Following are the libraries supported by OneCompiler's Python compiler

NameDescription
NumPyNumPy python library helps users to work on arrays with ease
SciPySciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation
SKLearn/Scikit-learnScikit-learn or Scikit-learn is the most useful library for machine learning in Python
PandasPandas is the most efficient Python library for data manipulation and analysis
MatplotlibMatplotlib is a cross-platform, data visualization and graphical plotting library for Python programming and it's numerical mathematics extension NumPy
DOcplexDOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling