The ultimate guide to senior engineering interviews. Strategies developed by FAANG engineers on Python concurrency, multithreading, monitors, and event loops. Prep faster for top company questions.

Concurrency in Python is one of the most complex and advanced topics brought up during senior engineering interviews. Knowledge of concurrency and multithreading can put interviewees at a considerable advantage. This course lays the foundations of advanced concurrency and multithreading and explains concepts such as Monitors, Event Loops and Deferred Callbacks in depth. It also builds simple and complete solutions to popular concurrency problems that can be asked about in interviews like the Reader-Writer Problem and the Dining Philosopher Problem. While prior knowledge of concurrency is not required to follow through with this course, familiarity with the very basic Python would be helpful.

Python Concurrency for Senior Engineering Interviews

<div class="card">
# Iterables
<div class="ans">
Iteration is defined as the repetition of a process or utterance.

## Iterable
In Python an iterable is an object which can be looped over its member elements using a for loop. An iterable is capable of returning its members one by one. The most common type of iterables in Python are sequences which include lists, strings and tuples. They support efficient access via indexing. The <code class="mthd">__getitem()__</code> can be invoked to return a member at the specified index. They also return their lengths via the <code class="mthd">__length()__</code> method. But not every type in Python is a sequence. Dictionaries, sets, file objects and generators aren't indexable but are iterable. Python also allows us to create iterables that are infinite called generators that we'll learn in later sections.

In order to qualify as an iterable, an object must define one of the two methods:

<ul>
<li>__iter__()</li>
<li>__getitem__()</li>
</ul>

Container objects need to define the <code class="mthd">__iter__()</code> method to support iteration. The <code class="mthd">__iter__()</code> method returns an iterator for the object. Using the iterator we can sequentially access the elements of an iterable object.

## Iterator
Iterator is an object which can be used to sequentially access the elements of an Iterable object. The iterator exposes <code class="mthd">__next__()</code> method in Python 3 and <code class="mthd">next()</code> in Python 2. Both the methods fetch the next element in sequence of the iterable object. An iterator must support the following methods

<ul>
<li>__iter__()</li>
<li>__next__()</li>
</ul>

The iterator object returns itself for the <code class="mthd">__iter__()</code> method. And this allows us to use the iterator and the iterable in a for loop

When the end of an iterable object's iteration is reached, <code class="mthd">next()</code> throws a <code class="clazz">StopIteration</code> exception. Put together, these rules are called the iterator protocol. The <code class="mthd">__iter__()</code> method for a container can also return something called a generator, which is also an iterator.


## Example
Study the code below which demonstrates iterable, iterator, and iteration in Python.


```python
if __name__ == "__main__":
 lstOfInts = list()
 lstOfInts.append(1)
 lstOfInts.append(2)
 lstOfInts.append(3)

 # get iterator of list using __iter__()
 it = lstOfInts.__iter__()
 print("iterator of list: " + str(it))

 # get member element of list using __getitem__()
 print("iterator of list: " + str(lstOfInts.__getitem__(2)))

 # iterator returns itself when passed to the iter function
 print("it is iter(it) = " + str(it is iter(it)))

 # get another iterator for list using the built in iter() method
 it_another = iter(lstOfInts)
 print("it_another = " + str(it_another))

 print("iteration using iterator in a for loop")
 # iterate using the iterator
 for element in it_another:
 print(element)

 print("iteration using iterable in a for loop")
 # iterate using the iterable
 for element in lstOfInts:
 print(element)

```


You can run the above code in the code widget below.
</div>
</div>

<div>
# Iterables
<div>
Iteration is defined as the repetition of a process or utterance.

## Iterable
In Python an iterable is an object which can be looped over its member elements using a for loop. An iterable is capable of returning its members one by one. The most common type of iterables in Python are sequences which include lists, strings and tuples. They support efficient access via indexing. The <code>__getitem()__</code> can be invoked to return a member at the specified index. They also return their lengths via the <code>__length()__</code> method. But not every type in Python is a sequence. Dictionaries, sets, file objects and generators aren't indexable but are iterable. Python also allows us to create iterables that are infinite called generators that we'll learn in later sections.

In order to qualify as an iterable, an object must define one of the two methods:

<ul>
<li>__iter__()</li>
<li>__getitem__()</li>
</ul>

Container objects need to define the <code>__iter__()</code> method to support iteration. The <code>__iter__()</code> method returns an iterator for the object. Using the iterator we can sequentially access the elements of an iterable object.

## Iterator
Iterator is an object which can be used to sequentially access the elements of an Iterable object. The iterator exposes <code>__next__()</code> method in Python 3 and <code>next()</code> in Python 2. Both the methods fetch the next element in sequence of the iterable object. An iterator must support the following methods

<ul>
<li>__iter__()</li>
<li>__next__()</li>
</ul>

The iterator object returns itself for the <code>__iter__()</code> method. And this allows us to use the iterator and the iterable in a for loop

When the end of an iterable object's iteration is reached, <code>next()</code> throws a <code>StopIteration</code> exception. Put together, these rules are called the iterator protocol. The <code>__iter__()</code> method for a container can also return something called a generator, which is also an iterator.


## Example
Study the code below which demonstrates iterable, iterator, and iteration in Python.


```python
if __name__ == "__main__":
 lstOfInts = list()
 lstOfInts.append(1)
 lstOfInts.append(2)
 lstOfInts.append(3)

 # get iterator of list using __iter__()
 it = lstOfInts.__iter__()
 print("iterator of list: " + str(it))

 # get member element of list using __getitem__()
 print("iterator of list: " + str(lstOfInts.__getitem__(2)))

 # iterator returns itself when passed to the iter function
 print("it is iter(it) = " + str(it is iter(it)))

 # get another iterator for list using the built in iter() method
 it_another = iter(lstOfInts)
 print("it_another = " + str(it_another))

 print("iteration using iterator in a for loop")
 # iterate using the iterator
 for element in it_another:
 print(element)

 print("iteration using iterable in a for loop")
 # iterate using the iterable
 for element in lstOfInts:
 print(element)

```


You can run the above code in the code widget below.
</div>
</div>

This lesson explains iterables in Python.

The Basics

Threading Module

Multiprocessing

Concurrent Package

Async.io

Global Interpreter Lock

Interview Practise Problems

The End

Iterables

Iterables