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A Guide to Threading in Python

In Computer Science, a thread is defined as the smallest unit of execution with the independent set of instructions. In simple terms, it is a separate flow of instruction. The advantage of threading is that it allows a user to run different parts of the program in a concurrent manner and make the design of the program simpler.  During threading, different processors run on a single program and each one of them performs an independent task simultaneously. However, if you want to perform multiprocessing, then you need to execute your code in a different language or use the multiprocessing module. In the CPython implementation of Python, interactions are made with the Global Interpreter Lock (GIL) which always limits one Python thread to run at a time. In threading, good candidates are considered those who spend much of their time waiting for external events. These are all true in the case when the code is written in Python. However, in the case of threading in C other than Python, they have the ability to release GIL and run in a concurrent manner.  Basically, building up your program to use threading will help to make the design clearer and easier to reason about. Let us see how to start a thread in Python. How to Start a Thread? The Python Standard Library contains a module named threading which comprises all the basics needed to understand the process of threading better. By this module, you can easily encapsulate threads and provide a clean interface to work with them.  If you want to start a thread, first you need to create a Thread instance and then implement .start(): import logging import threading import time def thread_func(name): logging.info("Thread %s: starting...",name) time.sleep(2) logging.info("Thread %s: finishing...",name) if __name__ == "__main__": format = "%(asctime)s: %(message)s" logging.basicConfig(format=format,level=logging.INFO, datefmt="%H:%M:%S") logging.info("Main    : before creating thread...") t = threading.Thread(target=thread_function,args=(1,)) logging.info("Main    : before running thread...") t.start() logging.info("Main    : wait for the thread to finish...") # t.join() logging.info("Main    : all done...")It is observable that the main section is responsible for creating and initiating the thread: t = threading.Thread(target=thread_function, args=(1,)) t.start()When a Thread is created, a function and a list of arguments to that function are passed. In the example above, thread_function() is being run and 1 is passed as an argument. The function, however, simply logs messages with a time.sleep() in between them.The output of the code above  will be displayed as:$ ./single_thread.py Main    : before creating thread... Main    : before running thread... Thread 1: starting... Main    : wait for the thread to finish... Main    : all done... Thread 1: finishing...The Thread gets finished only after the Main section of the code.Daemon ThreadsIn terms of computer science, a daemon is a computer program that runs as a background process. It is basically a thread that runs in the background without worrying about shutting it down. A daemon thread will shut down immediately when the program terminates. However, if a program is running non-Daemon threads, then the program will wait for those threads to complete before it ends.  In the example code above, you might have noticed that there is a pause of about 2 seconds after the main function has printed the all done message and before the thread is finished. This is because Python waits for the non-daemonic thread to complete. threading.shutdown() goes through all of the running threads and calls .join on every non-daemonic thread. You can understand it better if you look at the source of Python threading.  Let us the example we did before with a daemon thread by adding the daemon=True flag:t = threading.Thread(target=thread_function, args=(1,),daemon=True)Now if you run your program, the output will be as follows: $ ./daemon_thread.py  Main    : before creating thread...  Main    : before running thread...  Thread 1: starting...  Main    : wait for the thread to finish...  Main    : all done... The basic difference here is that the final line of output is missing. This is because when the main function reached the end of code, the daemon was killed.Multiple ThreadingThe process of executing multiple threads in a parallel manner is called multithreading. It enhances the performance of the program and Python multithreading is quite easy to learn.Let us start understanding multithreading using the example we used earlier:import logging import threading import time def thread_func(name): logging.info("Thread %s: starting...", name) time.sleep(2) logging.info("Thread %s: finishing...", name) if __name__ == "__main__": format = "%(asctime)s: %(message)s" logging.basicConfig(format=format,level=logging.INFO, datefmt="%H:%M:%S")     multiple_threads = list() for index in range(3): logging.info("Main    : create and start thread %d...",index) t = threading.Thread(target=thread_function,args=(index,)) threads.append(x) t.start() for index, thread in enumerate(multiple_threads): logging.info("Main    : before joining thread %d...",index) thread.join() logging.info("Main    : thread %d done...",index)This code will work in the same way as it was in the process to start a thread. First, we need to create a Thread object and then call the .start() object. The program then keeps a list of Thread objects. It then waits for them using .join(). If we run this code multiple times, the output will be as below: $ ./multiple_threads.py Main    : create and start thread 0... Thread 0: starting... Main    : create and start thread 1... Thread 1: starting... Main    : create and start thread 2...  Thread 2: starting...  Main    : before joining thread 0...  Thread 2: finishing...  Thread 1: finishing...  Thread 0: finishing...  Main    : thread 0 done...  Main    : before joining thread 1...  Main    : thread 1 done...  Main    : before joining thread 2...  Main    : thread 2 done... The threads are sequenced in the opposite order in this example. This is because multithreading generates different orderings. The Thread x: finishing message informs when each of the thread is done. The thread order is determined by the operating system, so it is essential to know the algorithm design that uses the threading process.  A ThreadPool ExecutorUsing a ThreadpoolExecutor is an easier way to start up a group of threads. It is contained in the Python Standard Library in concurrent.futures. You can create it as a context manager using the help of with statement. It will help in managing and destructing the pool. Example to illustrate a ThreadpoolExecutor (only the main section): import concurrent.futures if __name__ == "__main__":      format = "%(asctime)s: %(message)s"      logging.basicConfig(format=format,level=logging.INFO, datefmt="%H:%M:%S") with concurrent.futures.ThreadPoolExecutor(max_workers=3) asexecutor: executor.map(thread_function,range(3))The code above creates a ThreadpoolExecutor and informs how many worker threads it needs in the pool and then .map() is used to iterate through a list of things. When the with block ends, .join() is used on each of the threads in the pool. It is recommended to use ThreadpoolExecutor whenever possible so that you never forget to .join() the threads.The output of the code will look as follows:$ ./executor.py  Thread 0: starting... Thread 1: starting... Thread 2: starting... Thread 1: finishing... Thread 0: finishing... Thread 2: finishing…Race Conditions When multiple threads try to access a shared piece of data or resource, race conditions occur. Race conditions produce results that are confusing for a user to understand and it occurs rarely and is very difficult to debug.Let us try to understand a race condition using a class with a false database:class FalseDatabase: def race(self): self.value = 0 def update(self,name): logging.info("Thread %s: starting update...",name) local_copy_value = self.value local_copy_value += 1 time.sleep(0.1) self.value = local_copy_value logging.info("Thread %s: finishing update...",name)The class FalseDatabase holds the shared data value on which the race condition will occur. The function race simply intializes .value to zero.  The work of .update() is to analyze a database, perform some computation and then rewrite a value to the database. However, reading from the database means just copying .value to a local variable. Computation means adding a single value and then .sleep() for a little bit and then the value is written back by copying the local value back to .value().The main section of FalseDatabase:if __name__ == "__main__": format = "%(asctime)s: %(message)s" logging.basicConfig(format=format, level=logging.INFO, datefmt="%H:%M:%S") dtb = FalseDatabase() logging.info("Testing update. Starting value is %d...",dtb.value) with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor: for index in range(2): executor.submit(dtb.update,index) logging.info("Testing update. Ending value is %d...", dtb.value)The programs create a ThreadPoolExecutor with two threads and calls .submit()and then runs database.update()..submit() contains two arguments: both positional and named arguments are passed to the function running in the thread: .submit(function, *args, **kwargs)The output will look like as follows: $ ./racecond.py Testing unlocked update... Starting value is 0... Thread 0: starting update... Thread 1: starting update... Thread 0: finishing update... Thread 1: finishing update... Testing unlocked update... Ending value is 1...One ThreadIn this section, we would be discussing how threads work in a simplified manner.  When the ThreadPoolExecutor is informed to run each thread, we are basically telling it to which function to run and what are the parameters to be passed: executor.submit(database.update, index). This will allow each thread in the pool to call the executor.submit(index). The database is a reference to the FalseDatabase object that was created in main function.Each of the threads will have a reference to the database and also a unique index value which will make the log statements readable. The thread contains its own version of all the data local to the function. This is called local_copy in case of .update(). This is an advantage that allows all the local variables to a function to be thread-safe.Two ThreadsIf we consider the race condition again, the two threads will run concurrently. They will each point to the same object database and will have their own version of local_copy. The database object will be the reason for the problems.  The program will start with Thread 1 running .update() and then the thread will call time.sleep() and allows other threads to take its place and start running. Now Thread 2 performs all the same operations just like Thread 1. It also copies database.value into its local_copy but database.value does not get updated.  Now when Thread 2 ends, the shared database.value still contains zero and both versions of local_copy have the value one. Finally, Thread 1 again wakes up and it terminates by saving its local_copy which gives a chance to Thread 2 to run. On the other hand,  Thread 2 is unaware of Thread 1 and the updated database.value.  Thread 2 also then stores its version of local_copy into database.value.  The race condition occurs here in the sense that Thread 1 and Thread 2 have interleaving access to a single shared object and they overwrite each other’s results. Race condition can also occur when one thread releases memory or closes a file handle before the work of another thread. Basic Synchronization in ThreadingYou can solve race conditions with the help of Lock. A Lock is an object that acts like a hall pass which will allow only one thread at a time to enter the read-modify-write section of the code. If any other thread wants to enter at the same time, it has to wait until the current owner of the Lock gives it up.  The basic functions are .acquire() and .release(). A thread will call my_lock.acquire() to get the Lock. However, this thread will have to wait if the Lock is held by another thread until it releases it. The Lock in Python also works as a context manager and can be used within a with statement and will be released automatically with the exit of with block. Let us take the previous FalseDatabase class and add Lock to it:class FalseDatabase: def race(self): self.value = 0 self._lock = threading.Lock() def locked_update(self, name): logging.info("Thread %s: starting update...",name) logging.debug("Thread %s about to lock...",name) with self._lock: logging.debug("Thread %s has lock...",name) local_copy = self.value local_copy += 1 time.sleep(0.1) self.value = local_copy logging.debug("Thread %s about to release lock...",name) logging.debug("Thread %s after release...",name) logging.info("Thread %s: finishing update...",name)._lock is a part of the threading.Lock() object and is initialized in the unlocked state and later released with the help of with statement. The output of the code above with logging set to warning level will be as follows: $ ./fixingracecondition.py Testing locked update. Starting value is 0. Thread 0: starting update... Thread 1: starting update... Thread 0: finishing update... Thread 1: finishing update... Testing locked update. Ending value is 2.The output of the code with full logging by setting the level to DEBUG:$ ./fixingracecondition.py Testing locked update. Starting value is 0. Thread 0: starting update... Thread 0 about to lock... Thread 0 has lock... Thread 1: starting update... Thread 1 about to lock... Thread 0 about to release lock... Thread 0 after release... Thread 0: finishing update... Thread 1 has lock... Thread 1 about to release lock... Thread 1 after release... Thread 1: finishing update... Testing locked update. Ending value is 2.The Lock provides a mutual exclusion between the threads.The Producer-Consumer Threading ProblemIn Computer Science, the Producer-Consumer Threading Problem is a classic example of a multi-process synchronization problem.  Consider a program that has to read messages and write them to disk. It will listen and accept messages as they coming in bursts and not at regular intervals. This part of the program is termed as the producer.  On the other hand, you need to write the message to the database once you have it. This database access is slow because of bursts of messages coming in. This part of the program is called the consumer.  A pipeline has to be created between the producer and consumer that will act as the changing part as you gather more knowledge about various synchronization objects.  Using LockThe basic design is a producer thread that will read from a false network and put the message into the pipeline: import random Sentinel = object() def producer(pipeline): """Pretend we're getting a message from the network.""" for index in range(10): msg = random.randint(1,101) logging.info("Producer got message: %s",msg) pipeline.set_msg(msg,"Producer") # Send a sentinel message to tell consumer we're done  pipeline.set_msg(SENTINEL,"Producer")The producer gets a random number between 1 and 100 and calls the .set_message() on the pipeline to send it to the consumer: def consumer(pipeline):     """Pretend we're saving a number in the database.""" msg = 0 while msg is not Sentinel: msg = pipeline.get_msg("Consumer") if msg is not Sentinel: logging.info("Consumer storing message: %s",msg)The consumer reads a message from the pipeline and displays the false database.The main section of the section is as follows:if __name__ == "__main__": format = "%(asctime)s: %(message)s" logging.basicConfig(format=format,level=logging.INFO, datefmt="%H:%M:%S") # logging.getLogger().setLevel(logging.DEBUG) pipeline = Pipeline() with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor: executor.submit(producer, pipeline) executor.submit(consumer, pipeline)Now let us see the code of Pipeline that will pass messages from the producer to consumer: class Pipeline:  """Class to allow a single element pipeline between producer and consumer."""  def pipeline_message(self):  self.msg = 0 self.producer_lock = threading.Lock() self.consumer_lock = threading.Lock() self.consumer_lock.acquire() def get_msg(self, name): logging.debug("%s:about to acquire getlock...",name) self.consumer_lock.acquire() logging.debug("%s:have getlock...",name) msg = self.msg logging.debug("%s:about to release setlock...",name) self.producer_lock.release() logging.debug("%s:setlock released...",name) return msg def set_msg(self, msg, name): logging.debug("%s:about to acquire setlock...",name) self.producer_lock.acquire() logging.debug("%s:have setlock...",name) self.msg=msg logging.debug("%s:about to release getlock...",name) self.consumer_lock.release() logging.debug("%s:getlock released...", name)The members of Pipeline are: .msg - It stores the message to pass..producer_lock - It is a threading.Lock object that does not allow access to the message by the producer..consumer_lock - It is a threading.Lock that does not allow to access the message by the consumer.The function pipeline_message initializes the three members and then calls .acquire() on the .consumer_lock. Now the producer has the allowance to add a message and the consumer has to wait until the message is present.  .get_msg calls .acquire on the consumer_lock and then the consumer copies the value in .msg and then calls .release() on the .producer_lock. After the lock is released, the producer can insert the message into the pipeline. Now the producer will call the .set_msg() and it will acquire the .producer_lock and set the .msg and then the lock is released and the consumer can read the value. The output of the code with the logging set to WARNING: $ ./producerconsumer_lock.py Producer got data 43  Producer got data 45  Consumer storing data: 43  Producer got data 86  Consumer storing data: 45  Producer got data 40  Consumer storing data: 86  Producer got data 62  Consumer storing data: 40  Producer got data 15  Consumer storing data: 62  Producer got data 16  Consumer storing data: 15  Producer got data 61  Consumer storing data: 16  Producer got data 73  Consumer storing data: 61  Producer got data 22  Consumer storing data: 73  Consumer storing data: 22 Objects in Threading Python consists of few more threading modules which can be handy to use in different cases. Some of which are discussed below. Semaphore A semaphore is a counter module with few unique properties. The first property is that its counting is atomic which means that the operating system will not swap the thread while incrementing or decrementing the counter. The internal counter increments when .release() is called and decremented when .acquire() is called.  The other property is that if a thread calls .acquire() while the counter is zero, then the thread will be blocked until another thread calls .release(). The main work of semaphores is to protect a resource having a limited capacity. It is used in cases where you have a pool of connections and you want to limit the size of the pool to a particular number. Timer The Timer module is used to schedule a function that is to be called after a certain amount of time has passed. You need to pass a number of seconds to wait and a function to call to create a Timer:t = threading.Timer(20.0,my_timer_function) The timer is started by calling the .start function and you can stop it by calling  .cancel(). A Timer prompts for action after a particular amount of time.  Summary In this article we have covered most of the topics associated with threading in Python. We have discussed:What is Threading Creating and starting a Thread Multiple threading Race Conditions and how to prevent them Threading Objects We hope you are now well aware of Python threading and how to build threaded programs and the problems they approach to solve. You have also gained knowledge of the problems that arise when writing and debugging different types of threaded programs.  For more information about threading and its uses in the real-world applications, you may refer to the official documentation of Python threading.  To gain more knowledge about Python tips and tricks, check our Python tutorial and get a good hold over coding in Python by joining the Python certification course. 

A Guide to Threading in Python

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A Guide to Threading in Python

In Computer Science, a thread is defined as the smallest unit of execution with the independent set of instructions. In simple terms, it is a separate flow of instruction. The advantage of threading is that it allows a user to run different parts of the program in a concurrent manner and make the design of the program simpler.  

During threading, different processors run on a single program and each one of them performs an independent task simultaneously. However, if you want to perform multiprocessing, then you need to execute your code in a different language or use the multiprocessing module. 

In the CPython implementation of Python, interactions are made with the Global Interpreter Lock (GIL) which always limits one Python thread to run at a time. In threading, good candidates are considered those who spend much of their time waiting for external events. These are all true in the case when the code is written in Python. However, in the case of threading in C other than Python, they have the ability to release GIL and run in a concurrent manner.  

Basically, building up your program to use threading will help to make the design clearer and easier to reason about. Let us see how to start a thread in Python. 

How to Start a Thread? 

The Python Standard Library contains a module named threading which comprises all the basics needed to understand the process of threading better. By this module, you can easily encapsulate threads and provide a clean interface to work with them.  

If you want to start a thread, first you need to create a Thread instance and then implement .start()

import logging
import threading
import time

def thread_func(name):
     logging.info("Thread %s: starting...",name)
     time.sleep(2)
     logging.info("Thread %s: finishing...",name)

if __name__ == "__main__":
     format = "%(asctime)s: %(message)s"
     logging.basicConfig(format=format,level=logging.INFO,
                         datefmt="%H:%M:%S")
     logging.info("Main    : before creating thread...")
     t = threading.Thread(target=thread_function,args=(1,))
     logging.info("Main    : before running thread...")
      t.start()
     logging.info("Main    : wait for the thread to finish...")
     # t.join()
     logging.info("Main    : all done...")

It is observable that the main section is responsible for creating and initiating the thread: 

t = threading.Thread(target=thread_function, args=(1,))
t.start()

When a Thread is created, a function and a list of arguments to that function are passed. In the example above, thread_function() is being run and 1 is passed as an argument. The function, however, simply logs messages with a time.sleep() in between them.

The output of the code above  will be displayed as:

$ ./single_thread.py
Main    : before creating thread...
Main    : before running thread...
Thread 1: starting...
Main    : wait for the thread to finish...
Main    : all done...
Thread 1: finishing...

The Thread gets finished only after the Main section of the code.

Daemon Threads

In terms of computer science, a daemon is a computer program that runs as a background process. It is basically a thread that runs in the background without worrying about shutting it down. A daemon thread will shut down immediately when the program terminates. However, if a program is running non-Daemon threads, then the program will wait for those threads to complete before it ends.  

In the example code above, you might have noticed that there is a pause of about 2 seconds after the main function has printed the all done message and before the thread is finished. This is because Python waits for the non-daemonic thread to complete. 

threading.shutdown() goes through all of the running threads and calls .join on every non-daemonic thread. You can understand it better if you look at the source of Python threading.  

Let us the example we did before with a daemon thread by adding the daemon=True flag:

t = threading.Thread(target=thread_function, args=(1,),daemon=True)

Now if you run your program, the output will be as follows: 

$ ./daemon_thread.py 
Main    : before creating thread... 
Main    : before running thread... 
Thread 1: starting... 
Main    : wait for the thread to finish... 
Main    : all done... 

The basic difference here is that the final line of output is missing. This is because when the main function reached the end of code, the daemon was killed.

Multiple Threading

Multiple Threading Process in Python

The process of executing multiple threads in a parallel manner is called multithreading. It enhances the performance of the program and Python multithreading is quite easy to learn.

Let us start understanding multithreading using the example we used earlier:

import logging
import threading
import time

def thread_func(name):
    logging.info("Thread %s: starting...", name)
    time.sleep(2)
    logging.info("Thread %s: finishing...", name)

if __name__ == "__main__":
    format = "%(asctime)s: %(message)s"
    logging.basicConfig(format=format,level=logging.INFO,
                        datefmt="%H:%M:%S")

    multiple_threads = list()
    for index in range(3):
            logging.info("Main    : create and start thread %d...",index)
        t = threading.Thread(target=thread_function,args=(index,))
        threads.append(x)
        t.start()

    for index, thread in enumerate(multiple_threads):
        logging.info("Main    : before joining thread %d...",index)
        thread.join()
        logging.info("Main    : thread %d done...",index)

This code will work in the same way as it was in the process to start a thread. First, we need to create a Thread object and then call the .start() object. The program then keeps a list of Thread objects. It then waits for them using .join(). If we run this code multiple times, the output will be as below: 

$ ./multiple_threads.py
Main    : create and start thread 0...
Thread 0: starting...
Main    : create and start thread 1...
Thread 1: starting...
Main    : create and start thread 2... 
Thread 2: starting... 
Main    : before joining thread 0... 
Thread 2: finishing... 
Thread 1: finishing... 
Thread 0: finishing... 
Main    : thread 0 done... 
Main    : before joining thread 1... 
Main    : thread 1 done... 
Main    : before joining thread 2... 
Main    : thread 2 done... 

The threads are sequenced in the opposite order in this example. This is because multithreading generates different orderings. The Thread x: finishing message informs when each of the thread is done. The thread order is determined by the operating system, so it is essential to know the algorithm design that uses the threading process.  

A ThreadPool Executor

Using a ThreadpoolExecutor is an easier way to start up a group of threads. It is contained in the Python Standard Library in concurrent.futures. You can create it as a context manager using the help of with statement. It will help in managing and destructing the pool. 

Example to illustrate a ThreadpoolExecutor (only the main section): 

import concurrent.futures

if __name__ == "__main__":
     format = "%(asctime)s: %(message)s" 
     logging.basicConfig(format=format,level=logging.INFO,
                         datefmt="%H:%M:%S")
        with concurrent.futures.ThreadPoolExecutor(max_workers=3) asexecutor:
        executor.map(thread_function,range(3))

The code above creates a ThreadpoolExecutor and informs how many worker threads it needs in the pool and then .map() is used to iterate through a list of things. When the with block ends, .join() is used on each of the threads in the pool. It is recommended to use ThreadpoolExecutor whenever possible so that you never forget to .join() the threads.

The output of the code will look as follows:

$ ./executor.py 
Thread 0: starting...
Thread 1: starting...
Thread 2: starting...
Thread 1: finishing...
Thread 0: finishing...
Thread 2: finishing…

Race Conditions 

When multiple threads try to access a shared piece of data or resource, race conditions occur. Race conditions produce results that are confusing for a user to understand and it occurs rarely and is very difficult to debug.

Let us try to understand a race condition using a class with a false database:

class FalseDatabase:
    def race(self):
        self.value = 0

    def update(self,name):
        logging.info("Thread %s: starting update...",name)
        local_copy_value = self.value
        local_copy_value += 1
        time.sleep(0.1)
        self.value = local_copy_value
        logging.info("Thread %s: finishing update...",name)

The class FalseDatabase holds the shared data value on which the race condition will occur. The function race simply intializes .value to zero.  

The work of .update() is to analyze a database, perform some computation and then rewrite a value to the database. However, reading from the database means just copying .value to a local variable. Computation means adding a single value and then .sleep() for a little bit and then the value is written back by copying the local value back to .value().

The main section of FalseDatabase:

if __name__ == "__main__":
    format = "%(asctime)s: %(message)s"
    logging.basicConfig(format=format, level=logging.INFO,
                        datefmt="%H:%M:%S")
    dtb = FalseDatabase()
          logging.info("Testing update. Starting value is %d...",dtb.value)
          with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
          for index in range(2):
              executor.submit(dtb.update,index)
    logging.info("Testing update. Ending value is %d...", dtb.value)

The programs create a ThreadPoolExecutor with two threads and calls .submit()and then runs database.update().

.submit() contains two arguments: both positional and named arguments are passed to the function running in the thread: 

.submit(function, *args, **kwargs)

The output will look like as follows: 

$ ./racecond.py
Testing unlocked update... Starting value is 0...
Thread 0: starting update...
Thread 1: starting update...
Thread 0: finishing update...
Thread 1: finishing update...
Testing unlocked update... Ending value is 1...

One Thread

In this section, we would be discussing how threads work in a simplified manner.  

When the ThreadPoolExecutor is informed to run each thread, we are basically telling it to which function to run and what are the parameters to be passed: executor.submit(database.update, index)This will allow each thread in the pool to call the executor.submit(index). The database is a reference to the FalseDatabase object that was created in main function.

Each of the threads will have a reference to the database and also a unique index value which will make the log statements readable. The thread contains its own version of all the data local to the function. This is called local_copy in case of .update(). This is an advantage that allows all the local variables to a function to be thread-safe.

Two Threads

If we consider the race condition again, the two threads will run concurrently. They will each point to the same object database and will have their own version of local_copy. The database object will be the reason for the problems.  

The program will start with Thread 1 running .update() and then the thread will call time.sleep() and allows other threads to take its place and start running. Now Thread 2 performs all the same operations just like Thread 1. It also copies database.value into its local_copy but database.value does not get updated.  

Now when Thread 2 ends, the shared database.value still contains zero and both versions of local_copy have the value one. Finally, Thread 1 again wakes up and it terminates by saving its local_copy which gives a chance to Thread 2 to run. On the other hand,  Thread 2 is unaware of Thread 1 and the updated database.value.  Thread 2 also then stores its version of local_copy into database.value.  

The race condition occurs here in the sense that Thread 1 and Thread 2 have interleaving access to a single shared object and they overwrite each other’s results. Race condition can also occur when one thread releases memory or closes a file handle before the work of another thread. 

Basic Synchronization in Threading

You can solve race conditions with the help of Lock. A Lock is an object that acts like a hall pass which will allow only one thread at a time to enter the read-modify-write section of the code. If any other thread wants to enter at the same time, it has to wait until the current owner of the Lock gives it up.  

The basic functions are .acquire() and .release(). A thread will call my_lock.acquire() to get the Lock. However, this thread will have to wait if the Lock is held by another thread until it releases it. 

The Lock in Python also works as a context manager and can be used within a with statement and will be released automatically with the exit of with block. Let us take the previous FalseDatabase class and add Lock to it:

class FalseDatabase:
    def race(self):
        self.value = 0
        self._lock = threading.Lock()

    def locked_update(self, name):
        logging.info("Thread %s: starting update...",name)
        logging.debug("Thread %s about to lock...",name)
        with self._lock:
            logging.debug("Thread %s has lock...",name)
            local_copy = self.value
            local_copy += 1
            time.sleep(0.1)
            self.value = local_copy
            logging.debug("Thread %s about to release lock...",name)
       logging.debug("Thread %s after release...",name)
       logging.info("Thread %s: finishing update...",name)

._lock is a part of the threading.Lock() object and is initialized in the unlocked state and later released with the help of with statement. 

The output of the code above with logging set to warning level will be as follows: 

$ ./fixingracecondition.py
Testing locked update. Starting value is 0.
Thread 0: starting update...
Thread 1: starting update...
Thread 0: finishing update...
Thread 1: finishing update...
Testing locked update. Ending value is 2.

The output of the code with full logging by setting the level to DEBUG:

$ ./fixingracecondition.py
Testing locked update. Starting value is 0.
Thread 0: starting update...
Thread 0 about to lock...
Thread 0 has lock...
Thread 1: starting update...
Thread 1 about to lock...
Thread 0 about to release lock...
Thread 0 after release...
Thread 0: finishing update...
Thread 1 has lock...
Thread 1 about to release lock...
Thread 1 after release...
Thread 1: finishing update...
Testing locked update. Ending value is 2.

The Lock provides a mutual exclusion between the threads.

The Producer-Consumer Threading Problem

In Computer Science, the Producer-Consumer Threading Problem is a classic example of a multi-process synchronization problem.  

Consider a program that has to read messages and write them to disk. It will listen and accept messages as they coming in bursts and not at regular intervals. This part of the program is termed as the producer.  

On the other hand, you need to write the message to the database once you have it. This database access is slow because of bursts of messages coming in. This part of the program is called the consumer.  

A pipeline has to be created between the producer and consumer that will act as the changing part as you gather more knowledge about various synchronization objects.  

Using Lock

The basic design is a producer thread that will read from a false network and put the message into the pipeline

import random
Sentinel = object()

def producer(pipeline):
    """Pretend we're getting a message from the network."""
    for index in range(10):
        msg = random.randint(1,101)
        logging.info("Producer got message: %s",msg)
        pipeline.set_msg(msg,"Producer")

    # Send a sentinel message to tell consumer we're done 
    pipeline.set_msg(SENTINEL,"Producer")

The producer gets a random number between 1 and 100 and calls the .set_message() on the pipeline to send it to the consumer

def consumer(pipeline):
    """Pretend we're saving a number in the database."""
    msg = 0
    while msg is not Sentinel:
       msg = pipeline.get_msg("Consumer")
       if msg is not Sentinel:
           logging.info("Consumer storing message: %s",msg)

The consumer reads a message from the pipeline and displays the false database.

The main section of the section is as follows:

if __name__ == "__main__":
    format = "%(asctime)s: %(message)s"
    logging.basicConfig(format=format,level=logging.INFO,
                        datefmt="%H:%M:%S")
    # logging.getLogger().setLevel(logging.DEBUG)

    pipeline = Pipeline()
         with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
         executor.submit(producer, pipeline)
         executor.submit(consumer, pipeline)

Now let us see the code of Pipeline that will pass messages from the producer to consumer

class Pipeline:
    """Class to allow a single element pipeline between producer and consumer.""" 
   def pipeline_message(self): 
     self.msg = 0
     self.producer_lock = threading.Lock()
     self.consumer_lock = threading.Lock()
     self.consumer_lock.acquire()

  def get_msg(self, name):
      logging.debug("%s:about to acquire getlock...",name)
      self.consumer_lock.acquire()
      logging.debug("%s:have getlock...",name)
      msg = self.msg
      logging.debug("%s:about to release setlock...",name)
      self.producer_lock.release()
      logging.debug("%s:setlock released...",name)
      return msg

  def set_msg(self, msg, name):
      logging.debug("%s:about to acquire setlock...",name)
      self.producer_lock.acquire()
      logging.debug("%s:have setlock...",name)
      self.msg=msg
      logging.debug("%s:about to release getlock...",name)
      self.consumer_lock.release()
      logging.debug("%s:getlock released...", name)

The members of Pipeline are: 

  • .msg - It stores the message to pass.
  • .producer_lock - It is a threading.Lock object that does not allow access to the message by the producer.
  • .consumer_lock - It is a threading.Lock that does not allow to access the message by the consumer.

The function pipeline_message initializes the three members and then calls .acquire() on the .consumer_lock. Now the producer has the allowance to add a message and the consumer has to wait until the message is present.  

.get_msg calls .acquire on the consumer_lock and then the consumer copies the value in .msg and then calls .release() on the .producer_lock. After the lock is released, the producer can insert the message into the pipeline. Now the producer will call the .set_msg() and it will acquire the .producer_lock and set the .msg and then the lock is released and the consumer can read the value. 

The output of the code with the logging set to WARNING

$ ./producerconsumer_lock.py
Producer got data 43 
Producer got data 45 
Consumer storing data: 43 
Producer got data 86 
Consumer storing data: 45 
Producer got data 40 
Consumer storing data: 86 
Producer got data 62 
Consumer storing data: 40 
Producer got data 15 
Consumer storing data: 62 
Producer got data 16 
Consumer storing data: 15 
Producer got data 61 
Consumer storing data: 16 
Producer got data 73 
Consumer storing data: 61 
Producer got data 22 
Consumer storing data: 73 
Consumer storing data: 22 

Objects in Threading 

Python consists of few more threading modules which can be handy to use in different cases. Some of which are discussed below. 

Semaphore 

A semaphore is a counter module with few unique properties. The first property is that its counting is atomic which means that the operating system will not swap the thread while incrementing or decrementing the counter. The internal counter increments when .release() is called and decremented when .acquire() is called.  

The other property is that if a thread calls .acquire() while the counter is zero, then the thread will be blocked until another thread calls .release()

The main work of semaphores is to protect a resource having a limited capacity. It is used in cases where you have a pool of connections and you want to limit the size of the pool to a particular number. 

Timer 

The Timer module is used to schedule a function that is to be called after a certain amount of time has passed. You need to pass a number of seconds to wait and a function to call to create a Timer:

t = threading.Timer(20.0,my_timer_function) 

The timer is started by calling the .start function and you can stop it by calling  .cancel(). A Timer prompts for action after a particular amount of time.  

Summary 

In this article we have covered most of the topics associated with threading in Python. We have discussed:

  • What is Threading 
  • Creating and starting a Thread 
  • Multiple threading 
  • Race Conditions and how to prevent them 
  • Threading Objects 

We hope you are now well aware of Python threading and how to build threaded programs and the problems they approach to solve. You have also gained knowledge of the problems that arise when writing and debugging different types of threaded programs.  

For more information about threading and its uses in the real-world applications, you may refer to the official documentation of Python threading.  To gain more knowledge about Python tips and tricks, check our Python tutorial and get a good hold over coding in Python by joining the Python certification course

Priyankur

Priyankur Sarkar

Data Science Enthusiast

Priyankur Sarkar loves to play with data and get insightful results out of it, then turn those data insights and results in business growth. He is an electronics engineer with a versatile experience as an individual contributor and leading teams, and has actively worked towards building Machine Learning capabilities for organizations.

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Top IT Certifications for Java Developers in 2021

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Ltd., Cognizant, Synopsys Inc., private universities, Mphasis, etc.Where to take Training for Certification: CPP Institute has all the study resources you need to prepare for this examination. Apart from that, you can study from YouTube free resources.Who should take the Training (roles) for Certification: Any programmer or computer science aspirant - who wants to expand their knowledge of C/C++ or start their career as a C/C++ programmer or developer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fees for Certification:CLA Certification: $ 147.50 (50% discount voucher)CPA Certification: $ 147.50 (50% discount voucher)Exam fee for certification:CLA Certification: $ 295CPA Certification: $ 295Retake fee for certification: Aspirants who have paid the complete exam price (USD 295) or have completed a course aligned with certification in the self-study mode (50% discount voucher) can have a free retake of the CPA or CLA exam. There is no limit to the number of times a candidate may retake the exam. You must wait 15 days before being allowed to re-sit that exam.2. Oracle Certified Associate Java Programmer OCAJPThis is a Java programming certification provided by Oracle. Java is among the most popular programming languages. James Gosling is the creator of Java which was earlier named Oak. It is a robust, high-level, general-purpose, pure object-oriented programming language developed by Sun Microsystems (now part of Oracle). Java consistently tops the 'most used programming languages’ list and is one of the most extensively used software development platforms. If you have the plan to get a proper training course online before appearing for the certification exam, KnowledgeHut (https://www.knowledgehut.com/programming/java-training) has that for you.It is the preliminary and most basic certification provided by Oracle for Java. It helps gain fundamental understanding of Java programming and builds a foundation in Java and other general programming concepts. The certification encompasses two subcategories –OCAJP Java Standard Edition 8 (OCAJP 8) and  OCAJP Java Standard Edition 11 (OCAJP 11)It comprises of topics likeJava BasicsWorking with Java Data TypesUsing Operators and Decision ConstructsCreating and Using ArraysUsing Loop ConstructsWorking with Methods and EncapsulationWorking with InheritanceHandling ExceptionsClass Methods and EncapsulationDescribing and Using Objects and ClassesHandling ExceptionsJava Technology and the Java Development EnvironmentInheritance and InterfacesUnderstanding ModulesUsing Operators and Decision ConstructsWorking with Java ArraysWorking with Selected classes, Java Primitive Data Types and String APIsDemand and Benefits: Having an OCAJP certification verifies that the aspirant has all the necessary and essential skills to become an expert Java developer. This certification also helps in getting an internship or entry-level jobs in different organizations. The entry-level salary of a junior Java developer with this certification is $ 3670 per annum; when the candidate gathers two to three years of experience, the average salary hikes to $ 5430 annually.Top companies and industries hiring Oracle Certified Associate Java Programmers are Smart Monitor Pvt. Ltd., Fiserv, Micron Semiconductor Asia Pvt. Ltd., private universities and many others.Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for beginners in Java programming. It has workshops with hands-on learning and 40 hours of instructor-led online lectures. Apart from that, Oracle also provides exam vouchers for this certification course.Who should take the Training (roles) for Certification: Any programmer or computer science aspirant - who wants to settle as a Java developer or start his/her career as a Java programmer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fee for Certification: $ 245Application fee for certification:OCAJP8: $ 245OCAJP11: $ 249Exam fee for certification:OCAJP8: $ 245OCAJP11: $ 255Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days.3. Certified Associate in Python Programming (PCAP)Python is an interpreted, general-purpose, and high-level programming language developed by Guido Van Rossum. Python released in 1991 and within 5 to 6 years, this programming language become the most popular and widely used programming language in various disciplines. Today, companies use Python for GUI and CLI-based software development, web development (server-side), data science, machine learning, AI, robotics, drone systems, developing cyber-security tools, mathematics, system scripting, etc. PCAP is a professional Python certification credential that measures your competency in using the Python language to create code and your fundamental understanding of object-oriented programming.It comprises of topics likeBasic concepts of PythonOperators & data typesControl and EvaluationsModules and PackagesData AggregatesException HandlingStringsFunctions and ModulesObject-Oriented ProgrammingList Comprehensions, Lambdas, Closures, and I/O OperationsClasses, Objects, and ExceptionsDemand and Benefits: Having a Python certification verifies that the programmer or the aspirant has all the necessary and essential skills needed to become an expert Python developer. This certification also helps in getting an internship or entry-level jobs in different organizations. The average entry-level salary of a Python developer starts at around $100k per annum. With a few years of experience, the average salary hikes to $ 105k annually.Top companies and organizations hiring certified Python programmers are Bank of America, Atlassian, Google, Adobe, Apple, Cisco Systems, Intel, Lyft, IBM, etc.Where to take Training for Certification: KnowledgeHut has a fascinating course opportunity for beginners in Python programming. It has hands-on learning with 24 hours of instructor-led online lectures. Apart from that, the course has 100 hours of MCQs and three live projects.Who should take the Training (roles) for Certification: Any programmer, graduate, post graduate student, or computer science aspirant - who wants to pursue a career as a Python developer or  Python programmer can opt for this certification training. There is no other prerequisite to appear for this exam.Course fees for Certification:  $ 295Exam fee for certification: $ 295Retake fee for certification: If a candidate fails the exam, he/she has to wait for 15 days before being allowed to retake the exam for free. There is no limit to the number of times a candidate may retake an exam.4. MongoDB Certified Developer Associate ExamMongoDB is a NoSQL, document-based high-volume heterogeneous database system. Instead of having tables with rows and columns, MongoDB uses a collection of documents. It is a database development system that provides scalability and flexibility as per query requirements. Its document models are easy to implement for developers and can meet complex demands at scale.MongoDB created this MongoDB Certified Developer Associate Exam for individuals who require to verify their knowledge on fundamentals of designing and building applications using MongoDB. They recommend this certification for those who want to become software engineers and have a solid understanding of core MongoDB along with professional experience.It comprises of topics likeMongoDB BasicsCRUDIndexing and PerformanceThe MongoDB Aggregation FrameworkBasic Cluster AdministrationAggregation & ReplicationShardingMongoDB Performance  MongoDB for Python DevelopersMongoDB for Java Developers or MongoDB for JavaScript DevelopersData ModelingDemand and Benefits: Having a MongoDB Certified Developer Associate Exam certification verifies that the programmer or the aspirant has all the necessary and essential skills to become a NoSQL database expert. The MongoDB certification is inexpensive and in demand. The average salary for a software developer with MongoDB skills starts from $ 8200 per annum.Top companies and organizations hiring certified MongoDB developers are Accenture, Collabera, Leoforce LLC., Adobe, Trigent Software, Lyft, etc.Where to take Training for Certification: KnowledgeHut has a comprehensive course structure for those who want to learn MongoDB & Mongodb Administrator. It has 24+ hours of instructor-led online lectures and 80+ hours of hands-on with cloud labs. This self-paced course also includes capstone projects to give participants a feel of real world working.  Who should take the Training (roles) for Certification: Any programmer, graduate, post graduate student, experienced developer or computer science aspirant - who wants to embark on a career as a MongoDB developer or start his/her career as a NoSQL database expert or do better in their current role as a MongoDB developer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fees for Certification:  $ 150Exam fee for certification: $ 150Retake fee for certification: MongoDB University is no longer allowing a free retake with the exam fee. The candidate has to pay an additional $10 to reschedule or retake the exam.5. R Programming CertificationIt is a part of the data science specialization from Johns Hopkins University under Coursera. This course teaches R programming for efficient data analysis. It covers different R programming concepts like building blocks of R, datatypes, reading data into R from external files, accessing packages, writing functions, debugging techniques, profiling R code, and performing analysis.It comprises of topics like:Basic building blocks in RData types in RControl StructuresScoping Rules - OptimizationCoding StandardsDates and TimesFunctionsLoopingDebugging toolsSimulating data in RR ProfilerDemand and Benefits: Having an R Programming certification verifies that the programmer or the aspirant has all the necessary and essential skills require to get a job role as data analyst. This certification also helps in getting an internship or entry-level jobs in different organizations and firms. The average salary of a certified R programmer with this certification is ₹ 508,224 per annum.Top companies and industries hiring certified R programmers are Technovatrix, CGI Group Inc., Amazon, Sparx IT Solutions, Accenture, Uber, etc.Where to take Training for Certification: KnowledgeHut has a fascinating training course for those who wants to become a R programmer. It has 22+ hours of instructor-led live training and three self-paced live projects.Who should take the Training (roles) for Certification: Any data analyst, graduate, post graduate student, experienced data analyst or computer science aspirant - who wants to settle as a R programmer or data analyst can opt for this certification course. There is no other prerequisite to appear for this exam. Course fees for Certification: FreeFee for certification: $ 60 (Coursera Plus Monthly)Retake fee for certification: Free6. Oracle MySQL Database Administration Training and Certification (CMDBA)It is another course offered by Oracle for SQL developers. Oracle University designed this course for database administrators who want to validate their skills with developing performance, blending business processes, and accomplishing data processing work. Structured Query Language (SQL) is one of the top database management query languages that allows us to access and manipulate databases. If you want to verify your database skills during a job interview or impress your peers at your workplace then this certification is worth getting. This certification path includes Professional, Specialist, and Developer levels. The candidate should pass the MySQL Database Administrator Certified Professional Exam Part 1 & Part 2 to earn the certification.It comprises of topics likeInstalling MySQLMySQL ArchitectureConfiguring MySQLUser ManagementMySQL SecurityMaintaining a Stable SystemOptimizing Query PerformanceBackup StrategiesConfiguring a Replication TopologyDemand and Benefits: Having an CMDBA certification verifies that the programmer or the aspirant has all the necessary and essential skills required to get a job role as SQL developer. This certification also helps in getting an internship or entry-level jobs in different organizations and firms. The average salary of a certified MySQL DBA or backend developer with this certification is $ 66,470 per annum.Top companies and industries hiring Certified MySQL database administrators are Fiserv, IBM, HCL, Adobe, Microsoft, Apple, Accenture, Collabera, and more.Where to take Training for Certification: KnowledgeHut has a cutting-edge curriculum for those who want to become  MySQL database administrators. It has 16+ hours of instructor-led online lectures and 80+ hours of hands-on lab. Apart from that, this self-paced course has Capstone projects.Who should take the Training (roles) for Certification: Any developer, graduate, post graduate student, experienced developer or computer science aspirant - who wants to pursue a career as a DBA or backend developer or start his/her career in database management or backend software development can opt for this certification course. There is no other prerequisite to appear for this exam or course.Course fees for Certification: $ 255Exam fee for certification: $ 255Retake fee for certification: Aspirants can retake the exam if the exam voucher has a free retake option. If the exam retake option is available, one can opt for the exam after 14 days after the initial attempt.7. CCA Spark and Hadoop DeveloperWith the exponential growth in data, IT firms and organizations have to manage this tremendous amount of data generated. So, many companies are actively looking for Big data and Spark developers who can optimize performance. Big Data is the term used to describe enormous volumes of data. Apache Spark supports data management as it is an open-source centralized analytics engine that handles large-scale data processing.It requires prerequisite knowledge of Scala and Python. This certification also verifies and showcases your skills through Spark and Hadoop projects. Passing this certification course gives you a logo and a license to authenticate your CCA status.It comprises of topics likeLoad data from HDFS for use in Spark applicationsWrite the results back into HDFS using SparkRead and write files in a variety of file formatsPerform standard extract, transform, load (ETL) processes on data using the Spark APIUse metastore tables as an input source or an output sink for Spark applicationsUnderstand the fundamentals of querying datasets in SparkFilter data using SparkWrite queries that calculate aggregate statisticsJoin disparate datasets using SparkProduce ranked or sorted dataSupply command-line options to change your application configuration, such as increasing available memoryDemand and Benefits: Passing the CCA Spark and Hadoop Developer Exam (CCA175) by Cloudera verifies that you have all the essential skills required to get a job as a Hadoop developer and handle Big data projects. The average salary of a certified CCA Spark and Hadoop Developer with this certification is $ 74,200 per annum.Top companies and industries hiring Certified Spark and Hadoop Developers are Primus Global, IBM, Collabera, CorroHealth, Genpact, Xerox, Accenture, and more.Where to take Training for Certification: KnowledgeHut has extensive courses for those who want to become Big Data experts and want to work as Hadoop developers. It has different courses on Big Data Analytics, Apache Storm, Hadoop Administration, Apache Spark & Scala, Big Data with Hadoop, and more.Who should take the Training (roles) for Certification: Any Big Data developer, graduate & post graduate students, Hadoop developer or computer science aspirant - who wants to make a career in Big data development or start his/her career as a Big Data or Hadoop project developer can opt for this certification course. There is no other prerequisite to appear for this exam.Course fees for Certification: $ 295Application fee for certification: $ 295Exam fee for certification: $ 295Retake fee for certification: Within 30 to 60 minutes of exam completion, Cloudera will send a scorecard mail with a pass or fail status. If the candidate fails the exam, then they have to wait for 30 days for another try.  Cloudera gives additional discounts on retakes.ConclusionWhether you are starting your career as a coder or are an experienced programmer looking to grow in the industry, having a certification and proper knowledge of any popular programming language is one of the most proven ways to elevate your programming career.  We trust that this article will help you to understand your area of interest. Choose the programming language you wish to make a career in, wisely. This would also depend on your pre-existing knowledge. If you aren't sure which resource will be more informative for doing your certification as per your area of interest, KnowledgeHut (https://www.knowledgehut.com/) has all the support and expert trainers who can guide you, from start to finish—that is in clearing the exam and helping you gain sound knowledge of your preferred subject.Receiving a programming certification is an added bonus which will make you stand out from the rest. Proper training from an institute such as KnowledgeHut will help you gain skills that are relevant and in demand in the industry.
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Best Python Certifications of 2021

Programming is always at the core of computer science and Information Technology. Every year millions of programmers graduate with a degree and look for opportunities in the job market. The demand for programmers is growing exponentially, and this demand is not going anytime soon. Python was released by Python Software Foundation in 1991, and in just a few years, has become the most popular and widely used programming language in various disciplines.Python is an interpreted, general-purpose, and high-level programming language developed by Guido Van Rossum. Today, companies use Python for GUI and CLI-based software development, web development (server-side), data science, machine learning, AI, robotics, drone systems, developing cyber-security tools, mathematics, system scripting, etc.According to TIOBE index, Python ranks second among all other programming languages. KnowledgeHut has some fascinating advanced-level courses on Python, such as Machine Learning using Python and Artificial Intelligence using Python.Once you gain expertise in writing Python programs, candidates can start learning advanced-level Python libraries and modules such as Pandas, SciPy, NumPy, Matplotlib, etc. There are different options one can explore after learning Python. These are data analysis, machine learning, cybersecurity, automation, web scraping, etc.Top Python Certifications of 2021Certified Entry-Level Python Programmer (PCEP) Certified Associate in Python Programmer (PCAP) Introduction to Programming Using Python by Microsoft Certified Professional in Python Programming 1 & 2 (PCPP 1 & 2) Certified Expert in Python Programming (PEPP) During the course of your Python certification training and exam preparation, you will develop different real-world projects and get familiar with case studies. Also, there will be hands-on lab experiences in Python programming. In this article, you will get to know the top five Python certifications of 2021 that can give you the launchpad you need to embark on a successful career.   1. Certified Entry-Level Python Programmer (PCEP): The PCEP is an entry-level Python certification. To enroll in this course, you need to have a basic understanding of how procedural programming works. Also, some knowledge of flowcharts and algorithm creation will benefit you. Through this certification, an aspirant can gain the core and fundamental understanding of Python. This certification from the Python Institute will make you proficient in Python programming and help you become a Python certified professional. Aspirants and professionals can choose Python as a career option/path and climb the Python Institute’s certification ladder from associate to professional.PCEP comprises of topics like Basic formatting and outputting methods Handling Boolean values Compilation vs. interpretation Constants, Variables and Variable naming conventions Defining user-defined functions Fundamentals of computer programming Inputting and converting Data Logical vs. bitwise operations in Python Looping and control statements Lists New data aggregates: Tuples and Dictionaries The assignment operator Primary kinds of data and numerical operators Rules governing the building of expressions Working with multi-dimensional arrays Different slicing operations Demand and Benefits: Having a PCEP certification verifies that the programmer or the aspirant has knowledge of all the necessary and fundamental Python concepts. The course also covers all the syntax and semantics of different Python constructs & data types offered by the language. This course brings crisp knowledge on general coding techniques using standard language infrastructure and basic programming skills using Python. The average entry-level salary of a Python programmer with this certification will be $ 5660 per annum. Top companies and industries hiring PCEP are Philips, Cataleya Pvt. Ltd., Deloitte, Zynga, Mphasis, VMware, etc.Where to take Training for Certification: Python Institute has all the study resources you need to prepare for this examination. Apart from that, you can join the Python course offered by KnowledgeHut  that has 24 hours of instructor-led training covering the core programming concepts like operators, control flow, functions, syntax & indentations. Who should take the Training (roles) for Certification: Any programmer or computer science aspirant, who wants to learn Python or start an internship or entry-level job as Python programmer can opt for this certification course. There is no other prerequisite to appear for this exam. Course fees for Certification: $ 295 Application fee for certification: $ 295 Exam fee for certification: $ 295 Retake fee for certification: If a candidate fails the exam, he/she can wait 15 days before being allowed to retake the exam for free. There is no limit on the number of times a candidate may retake the exam.2. Certified Associate in Python Programmer (PCAP):PCAP is another important second-level or associate-level certification exam for Python. This course and certification will give you the confidence to measure your skill and complete the Python-based coding tasks. It also facilitates competing for competitive coding sessions. This course also comprises the essential notions and concepts related to object-oriented programming. With this associated-level certification, you can stand unique in the competitive job market. PCAP comprises of topics like Basic formatting and outputting methods Python basics Using Boolean values Compilation vs. interpretation Variables and variable naming conventions Defining and using functions Fundamentals of computer programming Fundamentals of OOP  How to use OOPs in the Python programming language Generators and closures Inputting and converting of data Logical vs. bitwise operations Looping and control statements File processing for Python developers Name scope issues New data aggregates: tuples and dictionaries Primary kinds of data and numerical operators Python modules Inheritance in Python Rules for creating expressions Working with multi-dimensional arrays Strings, lists, and other Python data structures The assignment operator The concept of exceptions and implementation Demand and Benefits: Having a PCAP certification verifies that the programmer or the aspirant has all the necessary and essential concepts of intermediate-level Python programming. The course also covers all the fundamental concepts of different Python constructs & fundamentals of OOP. This course brings crisp knowledge on general coding techniques using standard language infrastructure and basic programming skills using Python. The approximate salary of a Python programmer with this certification will be $7000 to $11,262 per annum. Top companies and industries hiring PCAP are CareCentrix, Accenture, Deutsche Bank, Collabera, NetApp, Capgemini, Tech Mahindra, Myntra, etc. Where to take Training for Certification: Python Institute (https://pythoninstitute.org/free-python-courses/) has all the study resources you need to prepare for this examination. You can also get a comprehensive training by enrolling for the Python course offered by KnowledgeHut (https://www.knowledgehut.com/programming/python-programming-certification-training) that has 24 hours of instructor-led training covering the core programming concepts like operators, control flow, functions, syntax & indentations. Who should take the Training (roles) for Certification: Any programmer or computer science aspirant, who wants to build a career in Python or pursue an associate-level job as a Python programmer or developer, can opt for this certification course. There is no other prerequisite to appear for this exam. Course fees for Certification: $ 295 Application fee for certification: $ 295 Exam fee for certification: $ 295 Retake fee for certification: If a candidate fails the exam, he/she can wait 15 days before being allowed to retake the exam for free. There is no limit to the number of times a candidate may retake an exam. 3. Introduction to Programming Using Python by MicrosoftIt is another popular entry-level Python certification by Microsoft (https://docs.microsoft.com/en-us/learn/certifications/exams/98-381). This certification covers all the syntax, data types, and basic understanding of Python. It also teaches how to logically solve any problem using Python constructs. Candidates wanting to enroll for this course are expected to have had some instruction or hands-on experience of approximately 100 hours with the Python programming language, including debugging skills, logic development, understanding conditional & decision-making statements, and maintaining well-formed well documented Python code. Microsoft’s Introduction to Programming Using Python comprises of topics like Basics of Python Using Boolean values Fundamentals of computer programming Interpretations Variables and variable naming conventions Defining and using functions Indexing and slicing operations Type conversions Basic formatting and outputting Data Types and Operators Control Flow with Decisions and Loops Construct Data structures Jump Statements Perform Input and Output Operations Document and Structure Code Comments and white-spaces Perform Operations Using Modules and Tools Demand and Benefits: Having a Microsoft certification verifies that the Python programmer or the aspirant has all the necessary and fundamental Python concepts. The course also covers all the syntax and semantics of different Python constructs & data types offered by the language. Anyone with this certification will have a better understanding of core Python, and the candidate can stand out in the competitive exams from the rest. The average entry-level salary of a Python programmer with this certification will be $ 5660 per annum. Top companies and industries hiring Python professionals with this credential are Cataleya Pvt. Ltd., Zynga, VMware, Mphasis, Deloitte, Capgemini, etc. Where to take Training for Certification: Microsoft has a paid five-day instructor-led course to prepare for this examination. Apart from that, you can join the Python course offered by KnowledgeHut that has 24 hours of instructor-led training covering the core programming concepts like operators, control flow, functions, syntax & indentations. Who should take the Training (roles) for Certification: Any programmer or computer science aspirant, who wants to learn Python or start an internship or entry-level job as Python programmer, can opt for this certification course. There is no other prerequisite to appear for this exam. Course fees for Certification: $ 127 Application fee for certification: $ 127 Exam fee for certification: $ 127 Retake fee for certification: Exam retake is free. If the candidate fails to achieve a passing score on the first attempt, he/she must wait 24 hours before retaking the exam. 4. Certified Professional in Python Programming 1 & 2 (PCPP 1 & 2):Once you sound knowledge of the core concepts of Python or have 3 to 5 years of experience in Python programming, you may prepare for professional Python certification. Certified Professional in Python Programming 1 certifications will reflect your experience and programming skills in the following areas: Text File Handling GUI-based Programming Encapsulation Inheritance Advanced Object-Oriented Programming PEP conventions Metaprogramming Communicating with a program's environment Using Libraries and Modules Importing math, science, and engineering modules Having this globally recognized credential will make you stand out in a competitive job market. Many recruiting agencies and firms are looking for professional Python programmers who can develop and deploy applications. Certified Professional in Python Programming 2 (PCPP2) is another advanced-level professional certification course offering proficiency in Python-MySQL database handling. Certified Professional in Python Programming 2 certification will reflect your experience and programming skills in the following areas: Basic directory structure CRUD operations Design patterns Observer and Proxy Singleton and State Design Template Method Model-View-Controller using Python Multiprocessing, threading, subprocess, and multiprocessor synchronization Relational database management using Python MySQL and SQL commands Sharing, storing and installing packages Network programming in Python Application testing techniques and principles Demand and Benefits: Having a PCPP certification verifies that the Python developer has all the necessary and essential skills of a professional Python programmer. The course covers all the advanced object-oriented programming concepts, GUI programming, etc. This course brings crisp knowledge for experienced professionals to make them stand out in the software development industry. The approximate salary of a Python programmer with this certification will be $ 12,053 to $ 14,700 per annum. Top companies and industries hiring PCPP certified professionals are Dell, Accenture, SG Analytics, HCL, Oracle, Capgemini, Tech Mahindra, Flipkart, etc. Where to take Training for Certification: Python Institute has all the study resources you need to prepare for this examination. Apart from that, you can join the Python course offered by KnowledgeHut that has 32 hours of instructor-led training covering the advanced programming concepts like database handling, OOPs, logical layout, data visualization, etc. Who should take the Training (roles) for Certification: Any professional, programmer, or experienced Python developer - who wants to settle as a senior Python developer or pursue an experienced-level job as a Python programmer or developer can opt for this certification course. The candidate should have the Certified Associate in Python Programmer (PCAP) certification or few years of work experience in Python. Course fees for Certification: $ 195 Application fee for certification: $ 195 Exam fee for certification: $ 195  Retake fee for certification: If a candidate fails the exam, he/she can wait 15 days before being allowed to retake the exam for free. There is no limit to the number of times a candidate may retake an exam. 5. Certified Expert in Python Programming (CEPP):This Python certification tag is for experts who complete all the OpenEDG Python Institute's Programming certification program (PCAP-31-xx, PCPP-32-1-xx, and PCPP-32-2-xx exams). It is the most advanced credential a Python developer can achieve from the Python Institute. Having this globally recognized credential will verify your expertise in Python programming. It highlights expertise in the universal concepts of Python programming. Also, this certification showcases the skills in resolving typical implementation challenges on different verticals of Python. Demand and Benefits: Having a CEPP certification verifies that the Python developer has industry level expertise in Python. This certification designates that the candidate has covered all the topics from basics to advance object-oriented programming concepts, GUI programming, etc. Using this certification, one can apply for a senior software development role, Python developer’s role, team lead, agile project management lead, and other senior job roles. Many professionals switch their careers to Big Data, Data Analytics, Machine learning, and deep learning after completing this certification. The approximate salary of a Python programmer with this certification will be $ 17,350 to $ 39,945 per annum. Top companies and industries hiring CEPPs are Amazon, Tesla, HSBC, Google, HCL, Oracle, Capgemini, Qualcomm, 6sense, Vitrana, and other top service-based companies. Where to take Training for Certification: Python Institute has all the study resources you need to prepare for PCAP-31-xx, PCPP-32-1-xx, and PCPP-32-2-xx examination. Once a candidate has passed all the certifications, he/she becomes recognized as an Open EDG Python Institute Certified Expert in Python Programming (CEPP). Who should take the Training (roles) for Certification: Any professional, Python expert, or senior Python developer, who wants to settle as a team lead or pursue an experienced-level job profile can opt for these certifications to reach at this level.  Course fees for Certification: $ 295 + $ 295 + $ 195 Application fee for certification: $ 295 + $ 295 + $ 195 Exam fee for certification: $ 295 + $ 295 + $ 195  Retake fee for certification: There is no retake fee Conclusion We trust this article gave you a better insight into different Python certifications! Whether you are starting out as a coder, or are an experienced Python programmer looking at making a splash in the industry, having a Python certification and proper knowledge of Python will elevate your programming career. Python is one of the top programming languages that can help you land different jobs in web development, app development, data science, cybersecurity, networking, web scraping, robotics, IoT, etc. If you aren't sure which online resource will be more informative for your Python certification, KnowledgeHut (https://www.knowledgehut.com/) has all the study materials and expert trainers who will help you reach the pinnacle of Python expertise. Receiving a Python certification, apart from academics and degrees, will make you stand out from the rest. So, start preparing for one today! 
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Best Python Certifications of 2021

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