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98-381 exam Dumps Source : Introduction to Programming Using Python

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Test denomination : Introduction to Programming Using Python
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: 40 existent Questions

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Microsoft Introduction to Programming Using

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    Introduction to Programming Using Python

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    Introduction to Python | killexams.com existent questions and Pass4sure dumps

    Instructor: Hiroki SayamaDate: Friday, March 1, 2019Time: 9 a.m. - 4 p.m. (Registration/coffee/light breakfast at 8:45 a.m., Lunch 12-1 p.m.)Location: Symposium Hall, Innovative Technologies knotty (ITC), Binghamton University85 Murray Hill Road, Vestal, NY 13850Credentials: A micro-credential (digital badge) will be issued to students who pass a group assignment at the finish of the day. Participation certificates will be issued to total participants.Who can grasp this course: This course is open to total engineers, professionals and students.

    REGISTER HERE ABOUT THE COURSE

    This course introduces computer programming in Python for those whohave tiny or no prior computer programming experience. The coursewill be heavily based on hands-on exercises, so participants arestrongly encouraged to bring their own laptops. The following topicswill be covered:

    TOPICS

    The following topics will be covered:

    1. Software installation and language basics2. Variables and data structures (strings, lists, matrices, dictionaries)3. tide control and office definition4. Plotting and visualization5. Using modules6. Object-oriented programming

    ABOUT THE INSTRUCTOR

    Hiroki Sayama is a Professor in the Department of Systems Science and Industrial Engineering. His research interests comprehend knotty dynamical networks, collective behaviors, companionable systems modeling, simulated life/chemistry, mathematical biology, computer and information sciences.

  • Director, heart for Collective Dynamics of knotty Systems
  • Director, Advanced Graduate Certificate Program in knotty Systems Science and Engineering
  • Professor, Faculty of Commerce, Waseda University, Japan
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    Thread Carefully: An Introduction To Concurrent Python | killexams.com existent questions and Pass4sure dumps

    The ability to execute code in parallel is crucial in a wide variety of scenarios. Concurrent programming is a key asset for web servers, producer/consumer models, batch number-crunching and pretty much any time an application is bottlenecked by a resource.

    It’s sadly the case that writing property concurrent code can be a existent headache, but this article aims to demonstrate how effortless it is to entrap started writing threaded programs in Python. Due to the large number of modules available in the standard library which are there to back out with this benign of thing, it’s often the case that simple concurrent tasks are surprisingly quick to implement.

    We’ll walk through the incompatibility between threads and processes in a Python context, before reviewing some of the different approaches you can grasp and what they’re best suited for.

    (Python 3 is used for the duration of the article.)

    It’s impossible to talk about concurrent programming in Python without mentioning the Global Interpreter Lock, or GIL. This is because of the large impact it has on which approach you select when writing asynchronous Python. The most necessary thing to note is that it is only a feature of CPython (the widely used “reference” Python implementation), it’s not a feature of the language. Jython and IronPython, among other implementations, acquire no GIL.

    The GIL is controversial because it only allows one thread at a time to access the Python interpreter. This means that it’s often not feasible for threads to grasp handicap of multi-core systems. Note that if there are blocking operations which befall outside Python, long-wait tasks fancy I/O for instance, then the GIL is not a bottleneck and writing a threaded program will noiseless be a benefit. However, if the blocking operations are largely crunching through CPython bytecode, then the GIL becomes a bottleneck.

    Why was the GIL introduced at all? It makes reminiscence management much simpler with no possibility of simultaneous access or race conditions, and it makes C extensions easier to write and easier to wrap.

    The upshot of total this is that if you exigency accurate parallelism and exigency to leverage multi-core CPUs, threads won’t Cut it and you exigency to exercise processes. A sever process means a sever interpreter with sever memory, its own GIL, and accurate parallelism. This pilot will give examples of both thread and process architectures.

    The concurrent.futures module is a well-kept furtive in Python, but provides a uniquely simple passage to implement threads and processes. For many basic applications, the effortless to exercise Pool interface offered here is sufficient.

    Here’s an instance where they want to download some webpages, which will be much quicker if done in parallel.

    """Download webpages in threads.""" import requests from concurrent.futures import ThreadPoolExecutor download_list = [ {'name': 'google', 'url': "http://google.com"}, {'name': 'reddit', 'url': "http://reddit.com"}, {'name': 'ebay', 'url': "http://ebay.com"}, {'name': 'bbc', 'url': "http://bbc.co.uk"} ] def download_page(page_info): """Download and save webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) if __name__ == '__main__': pool = ThreadPoolExecutor(max_workers=10) for download in download_list: pool.submit(download_page, download)

    Most of the code is just setting up their downloader example; it’s only the terminal obstruct which contains the threading-specific code. Note how effortless it is to create a dynamic pool of workers using ThreadPoolExecutor and submit a task. They could even simplify the terminal two lines to one using map:

    pool.map(download_page, download_list)

    Using threads works well in this case since the blocking operation that benefits from concurrency is the act of fetching the webpage. This means that the GIL is not an issue and threading is an model solution. However, if the operation in question was something which was CPU intensive within Python, processes would likely be more arrogate because of the restrictions of the GIL. In that case, they could acquire simply switched out ThreadPoolExecutor with  ProcessPoolExecutor.

    Whilst the concurrent.futures module offers a grandiose passage to entrap off the ground quickly, sometimes more control is needed over different threads, which is where the ubiquitous threading module comes in.

    Let’s re-implement the website downloader they made above, this time using the threading module.

    """Download webpages in threads, using `threading`.""" import requests import time import threading download_list = [ {'name': 'google', 'url': "http://google.com"}, {'name': 'reddit', 'url': "http://reddit.com"}, {'name': 'ebay', 'url': "http://ebay.com"}, {'name': 'bbc', 'url': "http://bbc.co.uk"} ] def status_update(): """Print 'Still downloading' at regular intervals.""" while True: print("Still downloading") time.sleep(0.1) def download_page(page_info): """Download and save webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) if __name__ == '__main__': for download in download_list: downloader = threading.Thread(target=download_page, args=(download,)) downloader.start() status = threading.Thread(target=status_update) status.start()

    For each thread they want to create, they manufacture an instance of the threading.Thread class, specifying what they would fancy their worker office to be, and the arguments required.

    Note that we’ve besides added a status update thread. The purpose of this is to repeatedly print “Still downloading” until we’ve finished fetching total the web pages. Unfortunately, since Python waits for total threads to finish executing before it exits, the program will never exit and the status updater thread will never stop printing.

    This is an instance of when the threading module’s army of options could be useful: they can imprint the updater thread as a daemon thread, which means that Python will exit when only daemon threads are left running.

    status = threading.Thread(target=status_update) status.daemon = True status.start()

    The program now successfully stops printing and exits when total downloader threads are finished.

    Daemon threads are generally most useful for background tasks and repetitive functions which are only required when the main program is running, since a daemon can be killed at any moment, causing data loss.

    So far we’ve only looked at cases where they know exactly what they want the threads to be working on when they start them. However, it’s often the case that they exigency to start a group of worker threads, then feed them tasks as they arrive.

    The best data structure for dealing with these tasks is, of course, a queue, and Python provides a queue module which is especially geared towards threading applications. FIFO, LIFO and priority queues are available.

    Using a queue.Queue object to add, get, and imprint tasks as done is as simple as this:

    from queue import Queue # maxsize=0 means eternal size limit tasks = Queue(maxsize=0) tasks.put("a task") tasks.put("another task") while not tasks.empty(): print(tasks.get()) # execute task tasks.task_done()

    Ok, that’s pretty basic so far. Now let’s exercise it to create a tasks queue for their website downloader. We’ll create a group of worker threads which can total access the queue and wait for tasks to approach in.

    """Download webpages in threads, using `threading` and `queue`.""" import requests import threading from queue import Queue NUM_WORKER_THREADS = 3 def download_page(page_info): """Download and save webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) def handle_tasks(tasks_queue): """Monitor tasks queue and execute tasks as appropriate.""" while True: download_page(tasks_queue.get()) tasks_queue.task_done() if __name__ == '__main__': tasks = Queue(maxsize=0) # Create and start worker threads for i in range(NUM_WORKER_THREADS): worker = threading.Thread(target=handle_tasks, args=(tasks,)) worker.daemon = True worker.start() # Add some tasks to the queue tasks.put({'name': 'google', 'url': "http://google.com"}) tasks.put({'name': 'reddit', 'url': "http://reddit.com"}) tasks.put({'name': 'ebay', 'url': "http://ebay.com"}) tasks.put({'name': 'bbc', 'url': "http://bbc.co.uk"}) tasks.join()

    Note that in this instance total the tasks were added in one evaporate for the sake of brevity, but in a existent application the tasks could trickle in at any rate. Here they exit the program when the tasks queue has been fully completed, using the .join() method.

    The threading module is grandiose for detailed control of threads, but what if they want this finer flat of control for processes? You might believe that this would be more challenging since once a process is launched, it’s completely sever and independent – harder to control than a new thread which remains within the current interpreter and reminiscence space.

    Fortunately for us, the Python developers worked difficult to create a multiprocessing module which has an interface that is almost identical to the threading module. This means that launching processes follows the exact identical syntax as their examples above. Their simple downloader would become this:

    """Download webpages in threads, using `multiprocessing`.""" import requests import time import multiprocessing download_list = [ {'name': 'google', 'url': "http://google.com"}, {'name': 'reddit', 'url': "http://reddit.com"}, {'name': 'ebay', 'url': "http://ebay.com"}, {'name': 'bbc', 'url': "http://bbc.co.uk"} ] def status_update(): """Print 'Still downloading' at regular intervals.""" while True: print("Still downloading") time.sleep(0.1) def download_page(page_info): """Download and save webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) if __name__ == '__main__': for download in download_list: downloader = multiprocessing.Process(target=download_page, args=(download,)) downloader.start() status = multiprocessing.Process(target=status_update) status.daemon = True status.start()

    We believe it’s awesome that Python manages to retain the same syntax between the threading and multiprocessing modules, when the action taking position under the hood is so different.

    When it comes to distributing data between processes, the queue.Queue that they used for threading will not travail between processes. This is because a queue.Queue is fundamentally just a data structure within the current process – albeit one which is cleverly locked and mutexed. Thankfully there exists a multiprocessing.Queue, which is specifically designed for inter-process communication. Behind the scenes, this will serialize your data and send it through a pipe between processes – a very convenient abstraction.

    Writing concurrent code in Python can be a lot of fun due to the inbuilt language features that abstract away a lot of problems. This doesn’t weigh in that a detailed flat of control cannot be achieved either, but rather that the barrier to getting started with simple tasks is lowered. So when you’re stuck waiting for one process to finish before starting the next, give one of these techniques a try.


    Practical Programming: An Introduction to Computer Science Using Python | killexams.com existent questions and Pass4sure dumps

    Jennifer Campbell et al

    Publisher

    The Pragmatic Bookshelf

    ISBN

    978-1-93435-627-2

    RRP

    £25.50

    Reviewed by

    Patrick Hill, CEng MBCS CITP

    Score

    8 out of 10

    Practical ProgrammingNot long ago, computer programming was often taught using strongly-typed, compiled languages such as Pascal, Modula and Ada. More recently, dynamic interpreted languages, such as Python and Ruby, acquire gained a tenacious following among software practitioners.

    This engage is an introduction to computer programming using the Python language. It is primarily aimed at first year computer science students and at professionals from non-IT fields who may find a grounding in computer programming techniques useful.

    The engage is broadly divided into two sections. The first section introduces elementary computer programming concepts and their corresponding Python implementations. In this section the reader is introduced to the fundamental components of imperative computer programs such as data types, variables, conditionals, iteration, collection types, files, functions and so forth. In addition the section introduces topics such as algorithms and efficiency, common programming patterns, exception handling and debugging techniques.

    The second section builds on these topics to provide standalone chapters that offer introductions to subjects such as remonstrate orientation, simple GUI implementation and relational databases.

    This is a very practical, hands-on benign of book. Each chapter contains illustrative in-line examples and concludes with a useful bullet-point summary and a set of exercises. Best-practice tips, common Python idioms and beginner’s ‘gotchas’ are scattered throughout the text, and source code for the examples is available from the book’s website.

    The authors avoid abstract concepts that are typical of many computer science texts. Rather, real-world applications are explored. The exercises are designed not only to review material that has been presented, but besides to hearten the reader to further explore the Python language, documentation and tools.

    This is, by design, an introductory text. Readers will find no mention of more advanced topics such as reflection, multi-threading, networking etc. Nonetheless, the engage provides a solid foundation from which these topics may be explored. I establish the engage to be an fascinating read and an inspiration to delve deeper into Python.

    Further information: The Pragmatic Bookshelf

    November 2009 



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