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python
Several good scripting languages for designing web, screen applications, and web services include Python and Ruby.
The linguists are very similar to one another in many aspects. Both give significant, object-oriented computing, an access terminal, standard packages, and persistent support to coders, and they are aesthetically fairly similar. However, due to the vast differences in their terminology and philosophies—primarily as a result of their distinct histories—Python and Ruby are poles apart in how they handle problem-solving.
It is important to consider which language to use for digital marketing because each has advantages and disadvantages, and your choice will have an impact.
constructing applications with Ruby
Ruby is a handy software program that is primarily used to create online apps. It is frequently used to construct servers and analyze data as well as for data extraction and indexing.
Ruby on Rails is the greatest framework for running Ruby, but it’s not alone in this. In 2004, Ruby on Rails got introduced, greatly simplifying the usage of the technology. Because of this, many start-up programmers choose to create their functionalities in Ruby.
Guido van Rossum developed Python, which was first made available in 1991. It arrived as the ABC syntax replacement, which was created to take the role of Basic and Pascal. It uses a lot of white space, which is a sign of its ideology, to make the code more readable. Python has been one of the coding languages with the quickest growth since it has so many applications, including data science, deep learning, and even institutional use.
Because its translators are accessible on the most widely used operating platforms and because it has a large number of modules, Python is very adaptable. Whenever it comes to script capitalization and white space, Python really becomes rigid. Even though it occasionally gets unpleasant, it also produces a very readable and simple language to learn.
The development of more automated processes and increased processing capacity has led to the development of more efficient AI, which has made AI a large topic in computer science today. Due in large part to frameworks like TensorFlow, Keras, and Theano that permit its inclusion in AI, this caused Python’s enormous surge in sales.
Workflow automation scripts are created in Python. Connecting software created in several languages which could be used to gather data is one of the most popular applications. When combined with infrastructure, Python programmers can create trading tools, and web applications, and are based on machine learning.
Both Python and Ruby are reliable platforms for website designing. Ruby provides Ruby on Rails, a Model-View-Controller (MVC) architecture-based framework. A common practice to segregate activity is the MVC paradigm. The Operator is the place where user requests that originate from the Perspective are executed and leverage the Template files to formulate a response, whereas the Model represents where the stuff is maintained and filtered, View is what displays the user his functionality and transmits the data, and
Python provides Django, which employs an MVC framework with a little modification: an MVT layout. Although the navigation is handled by the platform, the MVT design is quite comparable to an MVC model. The entire usability is handled by the template. The view is also used to undertake specific logic, communicate with a database, and create templates.
Ruby on Rails, which is led by Basecamp, transformed the sector previously controlled by Javas (J2EE) and.Net since it enabled quicker project creation. Because it was utilized by startups like Facebook, Airbnb, Reddit, and E-commerce, Ruby on Rails saw a spike in support. Although Ruby on Rails is indeed reliable and well-maintained by Basecamp, in addition to being a superb platform to build and produce web goods, it has fallen out of favor. Since it’s difficult to locate Ruby on Rails engineers, startups and businesses generally steer clear of beginning new projects using this technology.
Django was introduced in 2005 and is an accessible project maintained by the Django Open Source software. It became well-known for having qualities like flexible functionality, accessibility, and an MVC-like layout. Some of the most well-known websites, including Insta, Spotify, Youtube, and BitBucket, were built or updated using Django. Using the Django Rest platform, Django can also be used as an API in conjunction with a Jquery front-end approach.
Exquisite underpinnings for javascript are provided by both dialects. Since they accelerate the process and provide clean architecture and layers so you don’t really have to “start from scratch,” they are just a definite advantage over Java and.Net.
Python & Ruby are both elevated, object-oriented commands, therefore their effect is similar. Both of them provide dynamic shells, common modules, and persistent features. Both are also great options for online creation, primarily when you use Django for Python as well as Rails for Ruby, which were designed specifically for developing web tools. However, Django-Python is less well-known than Ruby on Rails as a digital marketing tool. Python is more popular in relevant scientific communities.
The same objectives are pursued by Ruby and Python when developing websites, but they travel multiple pathways to get there. For coders, Ruby is made to be extremely versatile and liberating. Making stuff accessible to the user is Python’s main purpose. This compromises some of Ruby’s attractiveness but offers Python a significant benefit when it pertains to modifying the code and effectively troubleshooting issues.
Their community members and regions of use are where they most significantly diverge. Ruby is most frequently used in conjunction with Ruby on Rails, which is Python’s Django. Ruby on Rails outsells Django in regard to demand and is almost equally as ubiquitous.
In areas of research outside of website designing, Python outperforms Ruby by a significant margin. Ruby does not have the same big stake in bioinformatics as Python. This may influence your decision to choose Python for digital marketing simply because you can seamlessly incorporate different packages that provide you with additional capabilities thanks to the same syntax.
They differ substantially in terms of execution ability to add more capabilities when it a better user experience. In terms of simulating the effect, both technologies provide comparable times for initiatives of the same size and terms of employment, however, overall Rails is 0.7% quicker. Even though the demand path, engineering, and subsequently the system frequently have a larger effect on this time, in this instance design has the greatest influence. Ruby on Rails loses ground against Django in full conformity. Since Django is developed in Python, it’s simpler to incorporate new capabilities and modules, such as artificial intelligence for data acquisition.
The vitality of a programmer is crucial, even though functionality and writing methodology are the main factors in selecting a certain language. Fortunately, there are vibrant ecosystems for both Python and Ruby.
Python seems to have a sizable Linux and intellectual community, thus it has a wide range of academic applications in mathematics and science. With more Python being utilized for software development, the ecosystem has consistency and complexity that only increase.
However, responsive web design has been the main focus of the Ruby majority’s attention since its inception. While it innovates more swiftly than the Python group, more things can go wrong as a result of this development. Additionally, despite becoming more diverse, it still falls short of Python in response to differences.
All these Ruby and Python offer promising potential for the future of enterprise applications. They definitely outperform the.NET and JAVA technologies in terms of how quickly applications can be developed.
They also provide qualities for review and abstraction, making them venues for developing applications that are prepared for the future. In conclusion, Python implies greater endurance than Ruby as it does not depend solely on website development.
In this article, we may say that both architectures are effective in assisting web programmers. Ruby, unlike Python, doesn’t truly expand outside web programming.
Ruby is the greatest option if all you want to do is construct websites. However, Python is the best choice because it offers modules that allow additional fields if you plan to add other capabilities to your web apps, such as computer vision or other computer engineering topics.
Because Python may rely on resources outside of web design, we can also claim that it will last longer than Ruby.
The Django web development framework is based on Model View Template (MVT) architecture and written in Python to enable the rapid development of web applications. Once the client’s requirements are gathered, it is very easy to create a scalable, secure, and SEO-friendly web application.
Django is an open-source web framework that is highly in demand in the IT industry worldwide. The question that must be striking your mind now is why, after all, this framework is getting so much attention, what exactly makes it unique and the best for web development, so that it has become so popular lately.
So, Let’s dive in to find the answers to these questions. The following blog will introduce you to the features that make Django a powerful and unique web development framework. After reading it, you can decide if it fits your project needs.
Django is an open-source web development framework which means that it is freely available over the internet and the users can download and successfully use all its features without paying any cost. Using an open-source framework to build a web application automatically reduces the cost of development. Also, Its source code can be very easily downloaded from the public git repository or by writing this in the terminal.
git clone https://github.com/django/django.git
Or it can be installed directly in your project by just writing the given code inside the terminal.
pip install Django
One of the most essential characteristics of Django is its comprehensive documentation, which makes learning and mastering the framework a breeze. The documentation is very efficiently created using a modular approach in which the architecture on which Django is based, MVT, is kept in different components such as the Model has a different module and view and template has different modules, as well as many other important aspects of Django such as forms, Admin, Security, and Development process, have different modules, making it easier to navigate to the desired topic. There are also modules such as getting started tutorials for beginners and advanced tutorials for advanced users in the documentation.
This exceptionally well-organized documentation acts as a resource for developers, allowing them to quickly find answers to their questions. As a result, the amount of time spent searching queries has decreased significantly. In comparison to other open-source frameworks, Django has the best documentation.
Django’s tagline is “The web framework for perfectionists with deadlines.” which means that it is made to develop projects rapidly. Django has become extremely popular as a result of one of its primary features: rapid development. Django has capabilities that eliminate the need to create server files, files to connect to the server, and writing queries to create read, update, or delete database entries, allowing users to construct web applications at a rapid pace. It helps us construct web apps more quickly by reducing project implementation time.
Django is known for its rapid development, as stated above, but how does it do so? The DRY concept, which underpins Django’s rapid development, stands for “Don’t Repeat Yourself.” Most of the time, developers spend the majority of their time managing the programs they’ve developed or rewriting various parts of the code. However, Django provides us with capabilities that eliminate unwanted repeats, allowing us to keep our code clean and manageable.
In a broader sense, Django divides the project into three modules: Model, View, and Template, with Model containing the database tables, View containing the data manipulation functions and acting as controllers, and Template containing the frontend parts such as HTML pages. Keeping our code as clean and manageable as possible.
Django is loaded with modules and libraries that make developers’ lives easier by handling most web development tasks, such as the authentication system it provides or the content administration system, which is the website’s administration site that can be built with just a few lines of code and makes the application very handy. It also lets us build our own URLs for our web pages and offers a variety of techniques for creating site maps, RSS feeds, and Atom feeds.
Another remarkable characteristic of Django is its high level of security. Big organizations like Instagram and Bitbucket, which require a high level of security to protect user data, rely on Django, which excels in this area. SQL injection, cross-site scripting, cross-site request forgery, and clickjacking are all examples of errors that developers make which it always prevents. It has various classes that allow developers to create an authentication system with a few lines of code and manage user accounts and passwords. It has inbuilt classes like Securitymiddleware that increase the security of WebApp.
A web application’s scalability refers to its capacity to function effectively as its size and volume grow to meet the needs of the client. Django is a scalable programming language that is currently used by companies such as Instagram, National Geographic, Spotify, Bitbucket, Eventbrite, Prezi, The Washington Times, and many others. Companies such as Disqus, Instagram, Pinterest, and Mozilla have been using Django for many years with no glitches or errors. Furthermore, data show that Django-powered websites have received up to 50 thousand hits per second, indicating Django’s scalability. Django’s versatile nature implies that he can swiftly switch from modest to large-scale projects.
The number of active users on platforms like Instagram is enormous, and all of those people are creating terabytes of data on a daily basis, which needs to be handled with precision, and Django’s use demonstrates that it is capable of doing so.
Django is a web development framework with a lot of flexibility. It may be used to create everything from simple apps with no database to complex applications with large databases. Django is now being used to create a wide range of web applications, including content management systems, social networking sites, and scientific computation applications. Instagram, National Geographic, Spotify, and Bitbucket, for example, are all from different domains, yet they all utilize Django currently.
Django’s SEO-optimized nature is another fantastic feature that sets it apart from other web frameworks.
SEO stands for Search Engine Optimization, and as the name implies, it involves adding features to your projects and optimizing them so that they appear towards the top of search results. As we all know, search engines employ algorithms that don’t always work well with site developers. Because we are developing our website in a human-readable format, they must add it to the server in URL format so that it may be recognized by search engines.
Django clarifies this notion by keeping the website using URLs rather than IP addresses on the server, making it simple for SEO engineers to add the website to the server while the web developer does not have to translate the URL into numeric code.
Django is one of the most popular web frameworks. It has a large and friendly community as well as channels for sharing and connecting. Django has been around for 13 years, which means it has been used by millions of people and improved by tens of thousands of programmers. It’s really easy to find a community of developers who are well-versed in Django and have coded with it.
Django has a large community that responds quickly to bugs and fixes them. Django is constantly improving as an open-source framework with the addition of new libraries. As a result, all of your questions are only a click away.
SQL (Structured Query Language) is a programming language that allows a web application to create a database and perform CRUD (Create, Read, Update, and Delete) operations on it.
Well, in Django, we have ORM to do the job; each class in the model represents a database table, and we don’t have to write SQL queries to manipulate the database, making life easier for developers with limited SQL knowledge.
Another two important and handy aspects of Django are its admin interface and inbuilt packages.
Django’s Admin Interface helps the clients to manage their applications without getting their hands dirty with the code. Django Packages supports the development process and makes our work easier. For example, Django-rest-framework helps us to easily build and use REST APIs.
After reading this blog post, you must have a good idea of the most important features of Django and whether or not they meet your requirements. Django is the best for everyone, whether you’re a developer or a client. We learned about some of Django’s features today, but these aren’t the only ones. “The sky’s the limit,” as they say. Django provides a lot more functionality, making it a good choice for your needs.
Startxlabs, one of India’s top digital transformation service providers. Launched in 2014, Startxlabs aims in innovating a digital future by developing technology for the web and mobile platforms. From our beginning as a technology development company, we’ve tried to stay true to our core beliefs and to deliver exceptional services to our clients. Whether it’s people we work for or people who work for us, we value honesty, passion, and the desire to explore. We have expertise in website development, android app development, iOS app development, Flutter, React Native app development, UI/UX design, and marketing strategy. With the engagement of our highly technical team, we have delivered over 110+ projects providing a positive impact on the users.
“Once a new technology rolls over you, if you’re not part of the steamroller, you’re part of the road.”
Stewart Brand
Technology is augmenting pre-existing business models, new technologies, Technology makes it possible for anyone to own a business,
Tech environment help economies move forward by building jobs and wealth. A blooming ecosystem helps developers to connect. They share stories about how to overcome issues they may be facing. Economic theories consider an evolutionary view of technological change and economic growth. Analyzing that vision of change and evolution of Technology, let’s emphasize its main characteristics:
Technology is dynamic. It changes and constantly improves. New varieties and new options appear persistently. Technological change is systemic. Technology doesn’t occur without any change in the ecosystem. New technologies emerge at once with the structure to manage and disburse them. The car needed highways and petrol pumps. The Internet has emerged with the fiber infrastructure. Nonetheless, that technological interdependence means that significant changes are slow and costly.
As per the idea of an evolving change in the Technology, growth provides better products, and are used to build the next generation of products. One can expect runaway technological evolution in the future, resulting in incomprehensible modifications to human civilization.
The global economy is surveyed to grow 3% per annum and double by 2038 to $150 trillion per year. If technology keeps on exploding, touching the 8% figure, then evolutionary technology sectors will be hitting $12 trillion in revenue in 2038, $10.4 trillion being net new revenue from today.
Nowadays, the adoption line of new technologies is practically vertical. Modernizations and technologies are rapidly introduced into the market and accepted by society. The time it takes for new Technology to reach mainstream adoption is accelerating exponentially, to the point in the future where new Technology can have more than 50% market penetration in few years, whereas before, it takes decades.
This is significant future growth for technology that indicates a very bright future for technology businesses, especially technology startups. We can also check by another perspective – the valuation of the companies responsible for the disturbance. Today we face a high rate of technology adoption. Other than this, the boost in new technologies is evolving over time. New disruptive recommendations appear constantly. A compelling division of the technology products that will build the $12 trillion in global annual GDP 20 years in the future likely doesn’t exist yet.
By addition, a startup consider risk and velocity. Whereas, a traditional company doesn’t like these two elements. Startups are thus the basic drivers of risk and velocity.
Startups offer disturbance based on the constant search for opportunities. Few of the technological future will also come from large businesses, but by and large, these large businesses still haven’t figured out how to accurately create upsetting innovation.
A strong implication that we are in the middle of the transfering the baton between the industrial and information era is the recent breakthrough reached in July 2016, where the world’s five largest public companies by market capitalization were all technology companies – Google, Apple, Amazon, Microsoft, and Facebook.
The aspect of the large established companies in the innovation landscape is largely as acquirers, where they grow developed products, using their capacity for effectiveness and scale. Whereas traditional businesses also see startups as a way of modernization. Corporate undertaking today has been elongated and established. Secured companies define challenges for the new startups, also they try to buy them when the risk is out.
Startups are much better at new upsetting invention than large businesses. Both factors (more disruptions and reduced diffusion time) introduce enormous challenges in traditional companies. The introduction of latest Technology means the minimizing of existing businesses.
Large tech businesses have established a more concerted relationship with startups. Organizations like Apple, Amazon, and Microsoft have displayed strong competency over the last 5 to 10 years in creating complementary relationships with startups by offering platforms and infrastructure for them to build on. Seperation cycles are running up.
New startups are build each year, and they acquire money from investors to grow. Many startups fail (and they are asked today to forget quickly to avoid absorbing excessive resources). But in those cases, entrepreneurs and investors try again. Repetition and diversification are crucial elements in startup environments. In cases where there is a success, the effort is observed through exits. An essential part of the money from these exits goes back into the system. Growing entrepreneurs also create new businesses or turn themselves into investors. A startup ecosystem is economical for a city, country, or region.
It is also sensitive to see investing in the creation of startup environments not just as a creator of economic capital and job creation but also as a hedge towards the social security of the future.
A tech ecosystem can be considered with five identifying features. This is uniform to the Financial Times’ glossary guide. Represented by the platform’s core components. Applications made by separate organizations complete them in the periphery offer solutions extensive system of use than the original platform owner constructed and manages a essential technical problem within an industry
Easy to create upon the core solution. It allows to develop the system of use. Authorize new and unexpected end uses attached to a core firm’s product. It is limited in value when used alone. Boosts substantially in value when used with the complementary applications
include well-known smartphone platforms, such as Apple and Android, but typical in social media platforms. They exist in industrial sectors. These sectors have core products in software, assembling, or scientific machinery. These manage as semi-autonomous, value-added resellers.
The term “Technology Ecosystem” can mean multiple things.
If you Google it, you will come across multiple purposes: it can describe a tech scene in a physical location, like London or San Francisco. A technology ecosystem is also the collection of tech solutions that a specific company uses to run its business. It is about how these solutions connect.
This is the explanation we will be working with here.
There is a condition we call it an ecosystem, as opposed to just a “collection of apps” or “app stack,” for example. This is because the term “ecosystem” explains what tools you are using and how they combine with one another. That same principle can be applicable to your technology ecosystem, the key is to figure out how all the fundamentals work together.
The utmost successful technology environments are unified in real-time, offering you to build more functionalities onto your core tools and expand their uses. As an example, let us communicate you run an online pet supplies store. You sell pet food, treats, accessories, cat litter, dog harnesses, and more.
It help if you likely had an eCommerce tool to run your business. You should have a payment portal to process customer payments and an accounting tool to keep track of your business finances. There should also be an email marketing app to send out newsletters and a CRM tool as a database for your customer data. The list goes on.
It is essential to keep moving beyond conventional tactics to reshape, rethink and reimagine business models and use technology as a disruptive market force if you want to stay ahead of the game or rather stay relevant in the game at all.
Organizations have to move from the traditional thinking of their span of control, and the tactics focusing only on – Front-office to back-office.
The cycle of influence today means that your customers could essentially be anywhere in the world, hence, your channels to reach and service them can’t be traditional.
Business models are themselves getting up-ende with concepts such as:
Nowadays, Startups have the advantage of being elegant to change procedures and upset the industry because they are more comfortable with letting technology overlook established businesses who are still evolving the ropes. Hence, it is easier for startups to create technology-run organizations.
to do that, keep the following in note:
Start by building a small, focused cell within your organization tasked with determining potential evolving technology. The head of this cell should think like an entrepreneur operating his startup within your startup. Let it to branch out on its own instead of looking to bring it back in.
This can bloom as an alternate revenue stream for your business too. You will have your team members line up to pay for this service which allows them stay updated on new technology and its application in their business model.
In the Peer-to-peer platforms serve as an excellent platform for entrepreneurs to keep in communication with the differences that embrace not just theirs but also a large part of the business environment.
As per the concept of “Trust Groups”, entrepreneurs have the opportunity to think extensively and grasp technology and business models that may have worked in a different industry and see if there are components that can be advantage for themselves.
In our case, ASCENT Foundation has offered us recognize the latest security challenges and arrangre advance solutions to help our businesses customers beat dangers in their environment.
In the article “How is Technology Beneficial for your Startups to Evolve?” , Through multiple conversations around all aspects of businesses, entrepreneurs can identify core challenges that might affect their professional and personal growth. With the support of the “Trust Group” members, they can remember these problems and find a practical solution to address these challenges.
Startxlabs, one of India’s top digital transformation service providers. Launched in 2014, Startxlabs aims in innovating a digital future by developing technology for the web and mobile platforms. From our beginning as a technology development company, we’ve tried to stay true to our core beliefs and to deliver exceptional services to our clients. Whether it’s people we work for or people who work for us, we value honesty, passion, and the desire to explore. We have expertise in website development, android app development, iOS app development, Flutter, React Native app development, UI/UX design, and marketing strategy. With the engagement of our highly technical team, we have delivered over 110+ projects providing a positive impact on the users.
Python is a general-purpose, high-level programming language that has many applications in web development, data science, software development, and more. It was initially released in the year 1991. Programming in Python involves simple syntax and it works on different platforms such as Windows, Mac, Linux, etc. In this article, we will discuss the Python functions.
Python Functions
A function is a set of related statements written to perform a specific task when it is called. It can take parameters and return a result. It is efficient to use several functions inside a program to perform specific tasks rather than merely writing a large program that performs the entire tasks. Functions are reusable and make the program easy to understand.
Syntax of Python Functions
A simple python function appears like
def func_name(parameters):
/* function statements */
return result
The syntax of a Python function can be defined as follows:
For example
def my_name(name):
" " " This function displays my name" " "
print("My name is" +name)
Function call
A function can be called in any part of the program or inside another function, by passing appropriate parameters to it.
>>>my_name('John')
My name is John
If you want to print the docstring of the function, you can print it by using,
>>>print(my_name._doc_)
This function displays my name
Return Statement
The ‘return’ statement is placed at the end of the function that returns either data of a specific type or the evaluated result of an expression. If there is no return statement specified as in the above example function, it returns nothing, or to be more precise, a none object is returned.
def sum(num1, num2):
" " " This function adds two numbers " " "
return num1+num2
print(sum(2,3))
Scope of variables and Lifetime
The availability of a variable whether inside or outside of a function is called its scope. A variable declared inside a function has recognition only inside the function and has no scope outside the function. Such variables are called the local variable. A variable declared on the python program (outside a function) is called a global variable and has a scope on the entire code and also inside the functions. You can also make a local variable global by adding the keyword “global” before the variable name.
Lifetime is the period of a variable that has its existence in memory. Variables declared within the function get destroyed when the function completes its execution. However, the global variables persist as long as the entire program is in execution.
We can see an example for local and global variables
def func1():
a=5 //local variable
return a
b=10 //global variable
c=func1()+b //function call
print("Value=" +c)
Output
15
def func1():
a=5 //local variable
return a
b=10 //global variable
c=func1()+b+a //function call
print("Value=" +c)
If you try executing this function it will show an error message like “name ‘a’ is not defined” as we used the variable outside its scope.
There is also another variable called non-local variables that are used within the nested functions. The non-local variables are declared using the ‘nonlocal’ keyword.
There are two types of functions available in Python. They are:
Types of Function Arguments
The values passed to the function as parameters are called the function arguments. There are two types of arguments. They are
For example,
def func1(num1,num2):
return num1+num2
func1(4) /* throws an error as the number of parameters during the function call is not equal to the number of parameters in the declaration */
def func1(num1=5,num2):
return num1+num2
func1(4)
func1(4,10)
Output
9
14
The function func1 has two arguments, one with the default value and another without any default value. It is mandatory to pass value to the num2 during every function call but passing a value to num1 is optional as it already has a default value and both the function calls will not throw any error here.
def func1(num1,num2):
return num1-num2
func1(num1=7,num2=6)
func1(num2=10,num1=17)
Output
1
7
def students(*stud_names):
print("The students are:")
for name in stud_names
print(name)
students("John","Mary","Harry")
Output
The students are:
John
Mary
Harry
Here we don’t know the number of arguments we are going to pass during the function call, so we use a for loop for retrieval of the values of arguments.
Python Recursive Functions
A recursive function is a function which calls itself during execution.
for example,
def factorial(num):
if num == 1:
return 1
else:
return (num * factorial(num-1))
number = 5
print("The factorial of", number, "is", factorial(number))
Sometimes it is hard to write a function only using iterations. There we can use recursive functions which are comparatively easier than iterative functions. However, sometimes for complex operations, recursive logic may be hard to understand.
Anonymous Function (Lambda Functions)
So far we have seen functions declared with a name followed by the def keyword. It is also possible to declare functions without a function name by using the lambda keyword and such functions are called the anonymous functions or the lambda functions. They are also used with some of the python built-in functions such as filter() and map().
The syntax of an anonymous function is
lambda arguments: expression
Arguments can be of any number, but there should be only one expression
int = lambda a: a + 5
print(int(10))
Output
15
In this article, we’ve learned about the functions in python, how to declare them, how to define them, the variables’ scope, their lifetime, types of arguments, and also the anonymous functions.
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Python is an object-oriented, interpreted programming language that is simple and easy to use that has significant applications in software development, web development, data science, and so on. The first thing you can learn in a programming language is its data types. In this article, we will explore the various data types of Python language.
Python has data types such as numbers, lists, tuple, string, set, and dictionary. We will discuss them one by one below.
Numbers
The first data type in this language is the number that has integers, float and complex data types defined as “int”, “float”, and “complex” classes. What differs integers from the float is the presence of decimal points in float. For example, 7 is an integer, and 7.0 is a floating variable. A complex data type consists of two parts, a real part, and an imaginary part. For example, x+iy is a complex number where x is the real part and y is the imaginary part. We can use two functions to determine the class to which a number belongs.
For example,
x = 3
print(type(x))
print(type(3.0))
c = 2 + 5j
print(isinstance(c, complex))
Output
<class 'int'>
<class 'float'>
True
The length of an integer data type can be of anything but the float data type has its accuracy up to 15 decimal places and after that it is inaccurate.
The number system such as binary(base 2), octal(base 8), and hexadecimal(base 16) can be represented using a set of prefixes such as
The process of converting one data type into another is called type conversion. Automatic conversion happens for certain operations. For example, adding an integer and float will automatically result in a float, as it implicitly converts the integer to float.
>>> 1.0 + 5
6.0
We can also explicitly convert a variable of one data type into another by calling some built-in python functions such as int(), float(), complex().
>>> int(2.2)
2
>>> int(-7.8)
-7
>>> float(9)
9.0
Decimal
When we want the most accurate calculations like financial calculations, where precision is so important, we can use decimal instead of float. It can be used by importing the decimal module.
For example,
The second print statement involving the use of a decimal module results in a number with more accurate precision.
Fractions
If we want to use fractions where the numerator and denominator both are integers, we can import the fraction module.
For example,
import fractions
print(fractions.Fraction(0.5))
print(fractions.Fraction(5,6))
Output
1/2
5/6
Math and Random
The math() and random() modules in python offer a wide range of calculations in trigonometry, logarithms, statistics, etc.
For example,
import math
import random
print(math.pi)
print(math.factorial(4))
print(random.randrange(1, 8))
Output
3.141592653589793
24
5
Lists
The list is one of the most used data types in python that stores a sequence of elements in it. We will discuss briefly how to create a list, how to access elements from it, indexing, and more.
List creation
A list can be created by,
list_name= [ ]
The elements contained inside the square brackets are called the items of a list. The list can be either empty, contains items of the same type, different types, or even a list that contains its items. That is, a list can be
list_name=[ ]
list_name=[ 1,2,3,4,5]
list_name=[1, apple, 2, red, 5.5]
list_name=[1, 2, [red, green, yellow], 3, 4, 5]
Accessing an element
The elements contained inside a list have an index associated with them. The indices start from 0 and should be integers. A list of size 6 (6 elements) has an index starting from 0 to 5. The elements can be accessed using their respective indices.
For example,
color_list = ['g', 'r', 'e', 'e', 'n']
print(color_list[0])
print(color_list [2])
Output
g
e
print(color_list [6]) //This throws an "IndexError" as we have used an index that is beyond the index range of this list
Negative Indexing
In python, lists are indexed with a negative index starting from the last item in the list.
For example,
color_list = ['g', 'r', 'e', 'e', 'n']
print(color_list[-1])
print(color_list[-2])
print(color_list[-3])
print(color_list[-4])
print(color_list[-5])
Output
n
e
e
r
g
List Slicing
A range of elements from a list in python can be accessed by using the “:” operator (slicing operator).
For example,
color_list = ['g', 'r', 'e', 'e', 'n']
print(color_list[2:4])
print(color_list[:-3])
print(color_list[3:])
print(color_list[:])
Output
['e', 'e', 'n']
['g', 'r']
['e', 'n']
['g', 'r', 'e', 'e', 'n']
Addition/ Modification
Lists elements can be added or modified (mutable) by using the ” = ” operator.
For example,
color_list = ['g', 'r', 'e', 'e', 'n']
color_list[0] = b
print(color_list)
color_list[1:4]=['l', 'a', 'c', 'k']
print(color_list)
Output
['b', 'r', 'e', 'e', 'n']
['b', 'l', 'a', 'c', 'k']
We can add elements to the list by using append(),insert() and extend() methods.
For example,
num_list=[1,2,3,4,5]
num_list.append(6) //adds one element at the last
num_list.extend([7,8,9]) //adds a number of elements at the last
print(num_list)
Output
['1', '2', '3', '4', '5', '6', '7', '8', '9']
We can also concatenate two lists by using the ‘+’ operator and multiply to a certain number of times using the ‘*’ operator.
Deletion
Deletion can be performed by using the keywords del, or by using the functions such as remove(), pop(), or clear().
For example
mylist=['1', '2', '3', '4', 5', '6']
del mylist(2) //deletes the element at index 2
del mylist[1:3] //deletes the elements from index1 to 3
del mylist //deletes the entire list
mylist=['a', 'b, 'c', 'd', e', 'f']
mylist.remove('a') //removes the specified element 'a'
mylist.pop //removes the top most(last) element 'f'
mylist.clear() //removes the entire list
Some of the other methods that can be used with the lists are sort(), count(), reverse(), and copy().
Using the “in” keyword, we can check whether an element is present in a list or not and also can be used in list iterations.
Tuples
Tuples are similar to lists in python but they are immutable (cannot be modified).
They can be created by placing the elements inside the () and separated by commas.
For example,
mytuple = (1, 2, 3)
The elements inside the tuples can be of the same data type or different. The tuples can be created with or without a bracket.
Accessing the tuple elements is the same as the list elements. They can be accessed using indexing, negative indexing, and slicing.
For example,
mytuple = (1, 2, 3)
print(mytuple[0]) //1
print(mytuple[-1]) //3
print(mytuple[1:2]) //2,3
Changing the elements in tuples can be done using the concatenation operator and deletion is done by using the keyword del.
Other methods can also be performed using the count(), index(), and a keyword for membership testing and iteration.
The major advantage of using tuples over lists is, iteration in tuples is easier as they are immutable.
Strings
A sequence of Unicode characters that are converted into binary characters for manipulation is called the strings in python. This process of converting a character into a binary number is called encoding and the reverse process is called decoding.
String creation
Strings are the characters that are enclosed within the quotes (either single or double).
For example,
string_name= 'My String'
print(string_name)
Output
My String
Accessing the characters inside a string is the same as accessing the list of tuples. They can be accessed by indexing, negative indexing, or slicing.
For example,
string_name= 'MyString'
print('string= ', string_name)
print('string[0] = ', string_name[0])
print('string[-1] = ', string_name[-1])
print('string[1:5] = ', string_name[1:5])
Output
string= MyString
string[0]= M
string[-1]= g
string[1:5]= yStri
Like tuples, strings are also immutable. We cannot modify or delete a particular character from a string, but we can delete the entire string by using the del keyword.
Some of the other operations performed in strings are concatenate (+), multiply(*), enumerate(), len(), format(), lower(), upper(), join(), split(), find(), and replace()
For example,
string_name= 'MyString'
>>>string_name.lower()
'mystring'
>>>string_name.upper()
'MYSTRING'
>>>My String.split()
['My', 'String']
>>>' '.join(['My', 'String'])
'My String'
>>>'My String'.replace('String','NewString')
'My NewString
>>>str1 = 'Hello'
>>>str2 ='How are you?'
>>>print('str1 + str2 = ', str1 + str2)
'Hello How are you?'
>>>print('str1 * 2 =', str1 * 2)
str1*2= HelloHello
Sets
A set is also a mutable data type that consists of unordered unique (immutable) elements in it.
They are used to perform operations like union, intersection, symmetric difference, and more.
Sets can be created by using {} brackets or by calling the set() function which is in-built. Sets can have any number of elements inside it and they can be of either the same or different data types.
For example,
my_set = {1, 2, 3}
print(my_set)
Output
{1, 2, 3}
Empty curly braces may be interpreted as a dictionary in python, so if we want to create an empty, we can do that by using the set() function.
Since sets are unordered, there is no index associated with the elements of sets. So we can add or delete an element from a set by using the functions such as add(), update(), discard() and remove().
For example,
set_name = {1, 3}
print(set_name) //1,3
set_name.add(2)
print(set_name) //1,2,3
set_name.update([2, 3, 4])
print(set_name) //1,2,3,4
set_name.discard(4) //1,2,3
Similarly, we can also use the pop() or clear() method to do the deletion operations.
Sets are used to perform operations like union, intersection, symmetric difference, and more.
For example,
X = {1, 2, 3, 4, 5}
Y = {4, 5, 6, 7, 8}
print(X | Y)
print(X & Y)
print(X ^ Y)
Output
{1,2,3,4,5,4,5,6,7,8}
{4,5}
{1,2,3,4,5,6,7,8}
There are many other built-in functions available to operate with sets. They are all(), any(), enumerate(), len(), max(), min(), sorted(), sum() and more.
There is a function called frozenset() using which can create immutable sets, cannot be changed once assigned.
For example,
X = frozenset([1, 2, 3, 4])
Dictionary
Dictionary is a data type that contains an unordered sequence of items in it and each of its elements is associated with a key/value pair.
A dictionary in python can be created by,
dict_name = {1: 'red', 2: 'green'}
Elements of a dictionary can be accessed by keys associated with them, like the indexes in lists and tuples. Dictionary elements can be accessed using the get() method.
Example:
dict_name = {1: 'red', 2: 'green'}
print(dict_name.get(2))
Output
green
A new addition to a dictionary is done by using the assignment (=) operator. If you are trying to assign a value for an already existing key, the value will get updated. Deletion from a dictionary is done by using methods such as pop(), popitem(), clear(), and del keyword.
There are some built-in functions available to use with the dictionaries. They are all(), any(), len(), cmp(), sorted().
In this article, we have learned about the different data types such as numbers, lists, tuples, strings, sets, and dictionaries along with their methods of addition, updation, how to access the elements from them, and more.
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