Learning Python — Advanced List Methods and Techniques

One of the most powerful data structures in Python is the List. I didn’t really believe that until I started working through the documentation and realized just how much work had been put into building the list data structure. Python lists natively support being utilized as queues, stacks, and arrays. This is why in order to Python like a pro, it is important to have a good understanding of lists. In this article, we will cover list comprehensions, the zip method, and the sort method.

List Comprehensions

Comprehensions are an advanced feature of Python lists that can help make code cleaner and easier to read. A composition is simply a way of performing a series of operations on a list using a single line. Comprehensions are denoted typically by the use of a for statement inside brackets. Here is a template for list comprehensions:

newList = [returned_value  for item in list condition_logic ]

Pulling out specific elements:

List comprehensions can be used to pull out certain elements that meet specific criteria. In the following example, we use a comprehension to pull out all the even numbers from a list.

# Create a list of numbers from 0 - 49
numRange = range(0,50)
# Pull out all the numbers that are even
evenNums = [num for num in numRange if num % 2 == 0 ]

In the above example, reading from left to right we are creating a new list with the num that is returned from the for loop where the remainder (% modulo) of the num divided by two is equal to zero. This is a common use case where all the even numbers need to be pulled from a list.

Perform an operation on elements:

List comprehensions can be used to perform operations on elements in a list. The following example shows how all the elements of a list could be squared.

# Create a list of numbers from 0 - 49
numRange = range(0,50)
# Pull out all the numbers that are even
evenNums = [num * num for num in numRange]

Limiting function calls using memoization:

This is one is a particularly useful bit of code that can save you from making expensive function calls more than necessary. The source was this post on stack overflow.

Memoization is the process of storing values in memory so that we don’t need to recompute the results later.

The case goes like this — you have a list that may potentially contain duplicate data or the function needs to be run to both check the output and to return the value. Here memoization can help by using a dictionary to keep track of the results of function calls with the same input parameters.

Memoize.py

def memoize(f):
    """ Memoization decorator for functions taking one or more arguments. """
    class memodict(dict):
        def __init__(self, f):
            self.f = f
        def __call__(self, *args):
            return self[args]
        def __missing__(self, key):
            ret = self[key] = self.f(*key)
            return ret
    return memodict(f)

# Initialize global function call variable
funcRuns = 0

# Wrap function in memoization wrapper
@memoize
def f(x):
  global funcRuns

  # Increment funcRuns every time the function is run
  funcRuns += 1
  return True

# Initialize numbers list
nums = [0,1,2,3,4,4]

# Run the list comprehension with 2 calls to f(x) per iteration
#   with 6 elements in the list and 2 calls per iteration this would 
#   normally yield 12 fuction executions. 
[f(x) for x in nums if f(x)]

# Log number of f(x) runs
print(funcRuns)

When you run the above example, you will find that the function is only run 5 times despite there being two calls to f(x) in the list comprehension and there being 6 elements in the list. It is only getting called once per unique number. Otherwise, the cached value is served up. If the function call is expensive, you can greatly speed up your code by memoizing the results.

This works well for reasonably sized lists to provide a speed improvement, but it could cause issues for very large lists as all the inputs/outputs are cached while the function is in scope, requiring the extensive use of memory to store values.

List Advanced Methods

Along with comprehensions, there are several other helpful methods available for lists. Here are a few that may be underutilized or otherwise unknown.

Zip(list, list2, …):

The zip method is used to combine multiple lists in Python into tuples. If the two lists are not the same length, then the longer of the two lists would be truncated to the length of the shorter.

first_names = ['John', 'Jeff', 'Chris']
last_names = ['Wick', 'Chen', 'Test', 'Truncated']
names = zip(first_names, last_names)
for name in names:
  print(name)
# Outputs: 
('John', 'Wick')
('Jeff', 'Chen')
('Chris', 'Test')

List.Sort(key=func, reversed=T/F):

I know. It seems strange putting the sort method into an article like this. I put it here because I feel that the use of sort with custom ranking functions is super underutilized. Here is my attempt at making a small sorting function that will return the top posts for a given day. I made sure to use a list comprehension at the end as well.

CustomListSort.py

posts = [
  {
    'Post': {
      'title':'Other today post',
      'date': 43750,
      'claps': 200
    }
  }, 
  {
    'Post': {
      'title':'Python Like a Pro - Lists and Their Many Uses',
      'date': 43750,
      'claps': 525
    }
  },
  {
    'Post': {
      'title':'Yesterdays news',
      'date': 43749,
      'claps': 25
    }
  }, 
]

# Rank here returns a tuple of the days
#   since 1900 date and the number of claps
def rank(element):
  return (element['Post']['date'], 
          element['Post']['claps'])

# Sorting using our rank algorithm 
#   and reversed so the largest date
#   with the most claps is first
posts.sort(key=rank, reverse=True)

# Finally a list comprehension to tie it all together
print([post['Post']['title'] for post in posts])

The output of this will be the following list where the most recent and highest-ranking article comes first followed by other articles and yesterdays. This does not account for when yesterday’s articles were performing well enough to necessitate a top spot still, but I think the point is made.

['Python Like a Pro - Lists and Their Many Uses', 
'Other today post', 
'Yesterdays news']

#python #programming

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Become a Python Programmer and learn one of employer’s most requested skills of 21st century!

This is the most comprehensive, yet straight-forward, course for the Python programming language on Simpliv! Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course we will teach you Python 3. (Note, we also provide older Python 2 notes in case you need them)

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Command Line Basics
Installing Python
Running Python Code
Strings
Lists
Dictionaries
Tuples
Sets
Number Data Types
Print Formatting
Functions
Scope
Built-in Functions
Debugging and Error Handling
Modules
External Modules
Object Oriented Programming
Inheritance
Polymorphism
File I/O
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Email sending
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Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services

Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

• revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
• microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
• Anaconda 4.2 – Anaconda is an opensource Python package

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Learn Python - Full Course for Beginners [Tutorial]

This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!
⭐️ Contents ⭐
⌨️ (0:00) Introduction
⌨️ (1:45) Installing Python & PyCharm
⌨️ (6:40) Setup & Hello World
⌨️ (10:23) Drawing a Shape
⌨️ (15:06) Variables & Data Types
⌨️ (27:03) Working With Strings
⌨️ (38:18) Working With Numbers
⌨️ (48:26) Getting Input From Users
⌨️ (52:37) Building a Basic Calculator
⌨️ (58:27) Mad Libs Game
⌨️ (1:03:10) Lists
⌨️ (1:10:44) List Functions
⌨️ (1:18:57) Tuples
⌨️ (1:24:15) Functions
⌨️ (1:34:11) Return Statement
⌨️ (1:40:06) If Statements
⌨️ (1:54:07) If Statements & Comparisons
⌨️ (2:00:37) Building a better Calculator
⌨️ (2:07:17) Dictionaries
⌨️ (2:14:13) While Loop
⌨️ (2:20:21) Building a Guessing Game
⌨️ (2:32:44) For Loops
⌨️ (2:41:20) Exponent Function
⌨️ (2:47:13) 2D Lists & Nested Loops
⌨️ (2:52:41) Building a Translator
⌨️ (3:00:18) Comments
⌨️ (3:04:17) Try / Except
⌨️ (3:12:41) Reading Files
⌨️ (3:21:26) Writing to Files
⌨️ (3:28:13) Modules & Pip
⌨️ (3:43:56) Classes & Objects
⌨️ (3:57:37) Building a Multiple Choice Quiz
⌨️ (4:08:28) Object Functions
⌨️ (4:12:37) Inheritance
⌨️ (4:20:43) Python Interpreter
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How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

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Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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