List comprehension with objects

Do you know the difference between the following syntax?

[x for x in range[5]]
[x for x in range[5]]
tuple[range[5]]

This is exactly what differentiates Python from other languages. Coming from functional languages and being implemented in Python from early days, list comprehension became its distinctive feature.

Lets dive deeper.

4 Facts About the Lists

First off, a short review on the lists [arrays in other languages].

• list is a type of data that can be represented as a collection of elements. Simple list looks like this [0, 1, 2, 3, 4, 5]
• lists take all possible types of data and combinations of data as their components:

>>> a = 12 >>> b = "this is text" >>> my_list = [0, b, ['element', 'another element'], [1, 2, 3], a] >>> print[my_list] [0, 'this is text', ['element', 'another element'], [1, 2, 3], 12]

• lists can be indexed. You can get access to any individual element or group of elements using the following syntax:

& >>> a = ['red', 'green', 'blue'] >>> print[a[0]] red

• lists are mutable in Python. This means you can replace, add or remove elements.

How to create lists

There are 2 common ways how to create lists in Python:

>>> my_list = [0, 1, 1, 2, 3]

And less preferable:

>>> my_list = list[]

Usually list[obj] is used to transform another sequence into the list. For example we want to split string into separate symbols:

>>> string = "string" >>> list[string] ['s', 't', 'r', 'i', 'n', 'g']

What is List Comprehension?

Often seen as a part of functional programming in Python, list comprehension allows you to create lists for a loop with less code. In short, its a truly Pythonic way of coding. Less code more effectiveness.

Lets look at the following example.

You create a list using a for loop and a range[] function.

& >>> my_list = [] >>> for x in range[10]: ... my_list.append[x * 2] ... >>> print[my_list] [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

And this is how the implementation of the previous example is performed using a list comprehension:

>>> comp_list = [x * 2 for x in range[10]] >>> print[comp_list] [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

The above example is oversimplified to get the idea of syntax. The same result may be achieved simply using list[range[0, 19, 2]] function. However, you can use a more complex modifier in the first part of comprehension or add a condition that will filter the list. Something like this:

>>> comp_list = [x ** 2 for x in range[7] if x % 2 == 0] >>> print[comp_list] [4, 16, 36]

Another available option is to use list comprehension to combine several lists and create a list of lists. At first glance, the syntax seems to be complicated. It may help to think of lists as outer and inner sequences.

Its time to show the power of list comprehension when you want to create a list of lists by combining two existing lists.

>>> nums = [1, 2, 3, 4, 5] >>> letters = ['A', 'B', 'C', 'D', 'E'] >>> nums_letters = [[n, l] for n in nums for l in letters] #the comprehensions list combines two simple lists in a complex list of lists. >>> print[nums_letters] >>> print[nums_letters] [[1, 'A'], [1, 'B'], [1, 'C'], [1, 'D'], [1, 'E'], [2, 'A'], [2, 'B'], [2, 'C'], [2, 'D'], [2, 'E'], [3, 'A'], [3, 'B'], [3, 'C'], [3, 'D'], [3, 'E'], [4, 'A'], [4, 'B'], [4, 'C'], [4, 'D'], [4, 'E'], [5, 'A'], [5, 'B'], [5, 'C'], [5, 'D'], [5, 'E']] >>>

Lets try it with text or, referring to it correctly, string object.

>>> iter_string = "some text" >>> comp_list = [x for x in iter_string if x !=" "] >>> print[comp_list] ['s', 'o', 'm', 'e', 't', 'e', 'x', 't']

The comprehensions are not limited to lists. You can create dicts and sets comprehensions as well.

>>> dict_comp = {x:chr[65+x] for x in range[1, 11]} >>> type[dict_comp] >>> print[dict_comp] {1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 7: 'H', 8: 'I', 9: 'J', 10: 'K'} >>> set_comp = {x ** 3 for x in range[10] if x % 2 == 0} >>> type[set_comp] >>> print[set_comp] {0, 8, 64, 512, 216}

When to Use List Comprehensions in Python

List comprehension is the best way to enhance the code readability, so its worth using whenever there is a bunch of data to be checked with the same function or logic for example, KYC verification. If the logic is quite simple, for instance, its limited with `true` or `false` results, list comprehension can optimize the code and focus on the logic solely. For example:

>>> customers = [{"is_kyc_passed": False}, {"is_kyc_passed": True}] >>> kyc_results = [] >>> for customer in customers: ... kyc_results.append[customer["is_kyc_passed"]] ... >>> all[kyc_results] False

There are many other ways how it can be implemented, but lets have a look at the example with list comprehension:

>>> customers = [{"is_kyc_passed": False}, {"is_kyc_passed": True}] >>> all[customer["is_kyc_passed"] for customer in customers] False

Benefits of Using List Comprehensions

List comprehensions optimize the lists generation and help to avoid side effects as gibberish variables.

As a result, you get more concise and readable code.

Difference Between Iterable and Iterator

It will be easier to understand the concept of generators if you get the idea of iterables and iterators.

Iterable is a sequence of data, you can iterate over using a loop. The easiest visible example of iterable can be a list of integers [1, 2, 3, 4, 5, 6, 7]. However, its possible to iterate over other types of data like strings, dicts, tuples, sets, etc.

Basically, any object that has iter[] method can be used as an iterable. You can check it using hasattr[]function in the interpreter.

>>> hasattr[str, '__iter__'] True >>> hasattr[bool, '__iter__'] False

Iterator protocol is implemented whenever you iterate over a sequence of data. For example, when you use a for loop the following is happening on a background:

• first iter[] method is called on the object to converts it to an iterator object.
• next[] method is called on the iterator object to get the next element of the sequence.
• StopIteration exception is raised when there are no elements left to call.

>>> simple_list = [1, 2, 3] >>> my_iterator = iter[simple_list] >>> print[my_iterator] >>> next[my_iterator] 1 >>> next[my_iterator] 2 >>> next[my_iterator] 3 >>> next[my_iterator] Traceback [most recent call last]: File "", line 1, in StopIteration

Generator Expressions

In Python, generators provide a convenient way to implement the iterator protocol. Generator is an iterable created using a function with a yield statement.

The main feature of generator is evaluating the elements on demand. When you call a normal function with a return statement the function is terminated whenever it encounters a return statement. In a function with a yield statement the state of the function is saved from the last call and can be picked up the next time you call a generator function.

>>> def my_gen[]: ... for x in range[5]: ... yield x

Generator expression allows creating a generator on a fly without a yield keyword. However, it doesnt share the whole power of generator created with a yield function. The syntax and concept is similar to list comprehensions:

>>> gen_exp = [x ** 2 for x in range[10] if x % 2 == 0] >>> for x in gen_exp: ... print[x] 0 4 16 36 64

In terms of syntax, the only difference is that you use parentheses instead of square brackets. However, the type of data returned by list comprehensions and generator expressions differs.

>>> list_comp = [x ** 2 for x in range[10] if x % 2 == 0] >>> gen_exp = [x ** 2 for x in range[10] if x % 2 == 0] >>> print[list_comp] [0, 4, 16, 36, 64] >>> print[gen_exp]

The main advantage of generator over a list is that it takes much less memory. We can check how much memory is taken by both types using sys.getsizeof[] method.

Note: in Python 2 using range[] function cant actually reflect the advantage in term of size, as it still keeps the whole list of elements in memory. In Python 3, however, this example is viable as the range[] returns a range object.

>>> from sys import getsizeof >>> my_comp = [x * 5 for x in range[1000]] >>> my_gen = [x * 5 for x in range[1000]] >>> getsizeof[my_comp] 9024 >>> getsizeof[my_gen] 88

We can see this difference because while `list` creating Python reserves memory for the whole list and calculates it on the spot. In case of generator, we receive only algorithm/ instructions how to calculate that Python stores. And each time we call for generator, it will only generate the next element of the sequence on demand according to instructions.

On the other hand, generator will be slower, as every time the element of sequence is calculated and yielded, function context/state has to be saved to be picked up next time for generating next value. That saving and loading function context/state takes time.

Final Thoughts

The very first thing that might scare or discourage a newbie programmer is the scale of educational material. The trick here is to treat each concept as an option offered by language, youre not expected to learn all the language concepts and modules all at once. There are always different ways to solve the same task. Take it as one more tool to get the job done.