The Python programming language was born in December 1989 during the Christmas holidays in Amsterdam, Holland. Guido van Rossum, a Dutch programmer then working at Centrum Wiskunde & Informatica, a mathematical and computer science research institute, decided for fun while spending the Christmas holidays on a new programming language that had been around for a long time. He wanted to write a commentary based on ABC.

ABC is a high-level, general-purpose programming language similar to BASIC and Pascal that was developed at the institute where Rossum worked. The purpose of creating ABC was to teach programming and prototyping, and because it was high-level (that is, it was similar to human language), it was easily read in English, and it was the best solution for teaching loops, logic, and data to beginners. Van Rossum had worked on the ABC project for several years and implemented many of its features in Python. The reason for choosing the name Python for this new language was his interest in the comedy series ” Monty Python Bird Circus “.

Khidou wanted the development of the application to be possible simply and without worrying about hardware, memory management, and such complications; So he thought of inventing his own programming language, inspired his ideas from ABC, and reducing the project time from three years to a three-month project; And that’s how Python was born.

But in 1995, a company called Zope was founded, specializing in the production of ad engines for the Internet. Zope created dynamic web pages written in Python, thus popularizing Python in its early days. Zope is run by a team of Python developers, joined by Van Rossum in 2000.

It was around this time that Van Rossum was nicknamed the “benevolent dictator for life” because he was the creator of this language and controlled its development stages. This nickname was later given to the leaders of text game projects who were the founders of the project themselves and had the final say in discussions and disagreements.

Released in October 2000, Python 2 quickly became popular in the systems industry as programmers were able to find creative ways to automate their processes. During this period, web development also experienced significant growth, and frameworks such as Jinja, Flask, and Django emerged, and large communities were immediately created for these frameworks.

In 2001, the Python Software Foundation was founded, an American non-profit organization dedicated entirely to the Python language. This foundation is also responsible for organizing the Python conference, which is held in 40 countries.

By 2010, Python-based frameworks were among the top ten, although the number of dynamic website competitors was increasing day by day so the 2000s can be called the peak years of Python. According to the TIOBE site ranking, in 2000, Python was the 20th most used language; By 2005, it climbed to the 6th place, and in April 2023, it finally reached the position of the copy. This website has selected Python as the “Programming Language of the Year” in 2007, 2010, 2018, 2020 and 2021.

Tim Peters, one of the main developers of Python, wrote a set of programming principles in 1999, known as the “Zen of Python”. Python developers and programmers are still trying to adhere to these principles. To view these principles in the Python interpreter, just enter the “import this” code to display this list:

•   Beautiful is better than ugly.
•   Explicit expression is better than implied.
•   Simple is better than complicated.
•   Complex is better than complicated.
•   Straight and smooth is better than nested.
•   Scattered is better than dense.
•   Readability is important.
•   Special cases are not special enough to break the rules.
•   Although the feasibility is more pure.
•   Errors should never be dismissed in silence.
•   Unless they are explicitly silenced.
•   When faced with ambiguity, avoid the temptation to guess.
•   There should be one (and preferably only one) clear way of doing things.
•   Although this method may not seem obvious at first unless you are Dutch.
•   Now is better than ever.
•   Although “never” is often better than “right now”.
•   If its implementation is hard to describe, it’s a bad idea.
•   If the implementation is easy to describe, it might be a good idea.
•   Namespaces are a great idea, let’s use them more!

When you write a program in C or C++, you must compile it; This means that you have to convert the code that is understandable for humans into a code that is understandable for computers. Machine code is actually low-level instructions that can be directly executed by the CPU. After the compilation process is completed successfully, your code will produce an executable file. Running this code will execute all the instructions you wrote step by step.

But Python is generally an interpreted language and not a compiled language, although compilation is one of the stages of the coding process with Python. Python code in the file py. It is written, first, it is compiled as bytecode and then in pic format. or pyo. is saved.

In fact, instead of being translated into machine code like C++, Python code is translated into bytecode. Bytecode is a set of low-level instructions that can be executed by an interpreter. On most computers, the Python interpreter is installed in the path usr/local/bin/python3.11/. Instead of executing instructions on the CPU, bytecode executes them on the virtual machine.

One of the advantages of interpreted languages ​​like Python is that they are independent of the operating system; This means that as long as the Python bytecode and the virtual machine are of the same version, this code can be run on any platform, including Windows or MacOS.

Think of the day when every user can program their own computer. We look to a future where every computer user will be able to “lift the hood” and improve the applications inside the computer. We believe this will fundamentally change the nature of software and software development.

These sentences were the proposal that the “Computer Programming for Everyone” project used to introduce itself. Van Rossum started this project to encourage people to program and he believed that the programming language should be so simple and understandable that every computer user can learn it easily.

Although Python language is slower than C and Java and is not suitable for designing applications that require high speed to run, such as heavy games, it has many advantages that have made it one of the most popular programming languages; including:

1. Easy to learn and use

Learning and using the Python language is very easy for beginners because it has a simple structure, readable codes, and commands very close to the English language, and compared to other languages, it requires writing much fewer lines of code to execute tasks.

2. A big and supportive Python community

Python was created more than 30 years ago, and since then the community of Python programmers has grown enough to support any developer at any level, whether a beginner or a professional. To learn Python, there are many free educational resources and videos in this forum and all over the Internet, and for this reason, people who choose this language to learn will not have to worry about the lack of resources.

3. The support of big sponsors

Programming languages ​​grow faster with the support of large companies. Facebook supports PHP, Oracle supports Java, and Microsoft supports Visual Basic and C#. Python language is also supported by Facebook, Amazon web services and especially Google. Since 2006, Google has chosen Python to develop many of its applications and platforms.

4. Hundreds of Python libraries and frameworks

Due to its large sponsors and active community, Python has a variety of unique libraries that save programmers time. There are many cloud multimedia services that support Python developers on different platforms through library tools.

5. Versatility, efficiency, reliability and speed

Python language can be used in various environments including mobile and desktop applications, web development, and hardware programming. Python’s versatility has made it the first choice of many programmers in various fields. Although the execution speed of programs written in Python is slightly lower than that of compiled languages ​​such as C, developing an application in Python takes much less time and takes up less space in memory.

6. Big data, machine learning, and cloud computing

After R, Python is the most popular programming language in the field of data science and analysis, because it is a very understandable language for many researchers who do not have a programming background. A large amount of data processing in companies is done only with Python. Most of the research and development projects are also done with the Python language, because Python has many uses, including the ease of analyzing and organizing usable data. Meanwhile, hundreds of Python libraries are used in thousands of machine-learning projects every day. Realizing the importance of Python, the hiring of Python programmers with mastery of data science principles has also increased a lot.

7. The flexibility of the Python language

Python is so flexible that it allows the developer to try a different project each time. Python does not limit developers to the development of specific applications and leaves them free to create any desired application. Also, migrating from JavaScript to Python is very easy for people who want to go from front-end to back-end, even though the two languages ​​are different.

8. Using Python in universities

Due to the use of Python in the field of artificial intelligence, deep learning, and data science, today this language is used to teach programming in schools and universities.

9. Automation capability

The many tools and modules that Python provides to the developer make the process of automating repetitive and boring tasks very easy and save time. Meanwhile, the number of lines of Python code for automation tool development is so small that it surprises the programmer.

10. Python is the language of startups

Ease of use, fast development, and low costs make Python a good choice for small startups with limited budgets. With the significant increase in the popularity of social media and the explosion of data in this platform, many startups active in the field of data analysis go to the Python language.

Large companies use the Django framework to save time and write less code in developing web applications. Django is a full-stack framework and is very popular because it is free and open-source. In fact, Django is so popular that if you go to a Python developer, wake him up, and ask him at gunpoint to design an app for you, you have no doubt that he will automatically switch to Django.

This framework includes all the necessary features by default, but its main feature is the emphasis on the principle of “avoid duplicate work”. Developers save time in the development of their projects with the help of Object-Relational Mapping, which is available in the Django framework.

Large companies and organizations that use the Django framework to build applications include NASA, Instagram, YouTube, and The Washington Post.

Flask falls under the category of microframeworks, which means it focuses on the bare minimum and leaves the rest to the developer. The Flask framework is a very suitable choice for people who know exactly what they want and want to have their hands open in designing web applications. This framework is also a good choice for emergency projects, medium to large scale. In cases where Django does not meet your needs in the development of web projects, you can go to Flask.

Famous brands that use Flask include Netflix, Lyft, Airbnb, Reddit, and Mailgun.

If you think that Flask doesn’t open your hands enough to design the application you want, go to Battle. Battle framework is a good choice for developing very small applications (for example, less than 500 lines of code) that do not require special features. Since Battle is a microframework, it only depends on the Python standard library.

Of course, keep this point in mind that in practice, using the Battle framework may interfere with your work; If you need to add a special feature to the application in the middle of the project, you will be in trouble, because Battle puts all the code in a single file. The battle framework is not suitable for developing large applications.

CherryPy is an open-source microframework for Python. Its minimal design is suitable for building web applications that can run on various platforms, including Windows, MacOS, Linux, and any other operating system that supports Python.

Cherry Pie is a good option for startups because it has few restrictions. This framework uses any type of technology for formatting, data access, etc., and it easily handles sessions, statistics, cookies, file uploads, and so on. The CherryPy community supports both beginners and professional developers.

Web2Py is a full-stack framework and is a good choice for developers and data scientists due to its data management capabilities. This framework is mostly used for projects related to data collection and analysis.

Python libraries

TensorFlow is an open-source library suitable for projects related to neural networks, computational graphs, and applications focused on machine learning. This library was created by Google in collaboration with the Brain Team deep learning artificial intelligence research team; For this reason, this library is present in almost all Google applications for machine learning.

The PsycheLearn library is for Python applications focused on machine learning and is ideal for validating supervised models on unseen data.  Scikit-Learn also provides an efficient approach for clustering, factor analysis, and principal component analysis for unsupervised neural networks and is a good choice in the field of image processing, such as feature extraction from images and texts.

Numpy is a library that other libraries such as TensorFlow use as their internal library to perform several operations. Since Python deals with applications in the data domain, Numpy helps developers a lot with its complex capabilities.

The main advantages are interactive features and ease of use. This library greatly simplifies complex mathematical implementations. If you are thinking of doing a project in the field of data science and machine learning, using the Numpy library will help you a lot.

Keras is a machine learning library in Python and provides a smooth mechanism for developing neural networks. Cress also offers best-in-class applications for model compilation, data set processing, graph visualization, and more.

This library is used in the development of backend applications based on Python. For example, Uber, Netflix, and Instacart use this library. In addition, startups with machine learning at the core of their product design have a special look at this library.

PyTorch is one of the largest machine learning libraries that allows developers to perform tensor calculations and performs well in the field of neural networks. If you are interested in natural language processing (NLP), the PyTorch library is a good choice for your projects.

Facebook developed this library in its artificial intelligence research group, and Uber uses it in the backend of its “Pyro” programming software. Since its inception, PieTorch has grown in popularity and attracted the attention of an increasing number of machine learning developers.

Learning the basics of Python is one thing, but what to do with this skill is another story and may become a challenge for some. Here we introduce 15 interesting and practical projects that can be developed with Python, which are good options to start with:

1. Organize files in the system

Python can be easily used to automatically organize files on the system. Operations such as renaming, copying, and moving hundreds of files can be done by writing a piece of Python code in a few seconds. For example, beets, a free and open-source software for organizing music files, uses Python and allows the user to manipulate the codes and even write the desired plug-in.

2. Listing

Using Python, you can save a list of your favorite websites on the Python command line instead of bookmarking them and moving them from one browser to another. For example, Buku bookmark management is written in Python 3 and besides managing the list of favorite websites, it has the possibility of automatic tagging, fixing broken links and searching in the database, and even locking and encrypting your lists.

This app is an open-source project and if you have an idea and don’t know what to do with it, you can add it as a new feature to this project so that other users can use it.

3. Creating a resume on a static website

Written in Python, Pelican is designed for building static websites and is a great choice for creating a clean yet interactive resume. In Pelican, you can access Python codes and modify them as much as you want.

4. Building dynamic websites

Python web frameworks such as Django and Flask will help you a lot to build dynamic websites with many features. For example, Instagram uses Django and Pinterest uses Flask, and both have the ability to manage high-resolution images, complex user interactions, and responsive web design elements, and use Python in their backend.

5. Data visualization

Python libraries provide a large set of data visualization tools to make it easier to examine data using graphs and maps. With the Python-based visualization library Seaborn and Matplotlib, you can easily display your data as graphs and maps, and use libraries like Bokeh to add more interactivity.

6. Construction of neural network

Companies like Uber use neural networks to communicate between passengers and drivers and even improve the quality of food and restaurant offers. Python language is at the center of these activities. According to Uber, the Pytorch deep learning library is the mainstay of the company’s algorithm development.

Python provides libraries such as Tensorflow and Cress for deep learning projects. By learning Python and using these libraries to build neural networks, you will gain a skill that will be useful in various projects for years to come.

7. Building a recommender engine

Another popular use of machine learning is the recommender engine. Python libraries such as NumPy and Scikit-Learn provide the user with a large set of diverse tools to create a platform for product offerings, for example, in online stores. For example, with the help of this data science stack and its combination with big data frameworks such as Apache Hadoop, Spotify, and Netflix can analyze data and suggest their favorite music and movies to users.

8. Analysis of user feedback

User sentiment analysis helps businesses make important decisions, and Python’s data science stack, its natural language toolbox (nltk), combined with simple, supervised learning algorithms can quickly identify comments, tweets, or any kind of feedback from Check the user side.

9. Collecting data from websites

Of course, many of these projects mentioned so far are not possible without data collection. With the help of Python and libraries and frameworks like Selenium , ScraPy and BeautifulSoup, you can easily extract information from different websites. Additionally, Python easily integrates with existing APIs, helping to pull structured data from websites quickly and efficiently.

10. Making mobile applications

More than 45% of the world’s population uses a smartphone, and for this reason, the mobile application market is always hot. With the help of the Kivy Python framework, you can develop applications that can be run on different operating systems. For example, Dropbox has used Python to build its mobile application, which runs without any problems on Windows, Mac OS, and even some Linux distributions.

11. Cryptocurrency exchange

With the help of Python, you can create a cryptocurrency trading robot that is active all the time and operates independently of the user. It is also possible to predict the best time to buy and sell cryptocurrency by combining machine learning algorithms in this bot. Even if you are not interested in buying and selling cryptocurrency yourself, your bot can have a high price in the market.

12. Making bots for social networks

With the help of Python, bots can be made to take over a large amount of your online activities on social networks. You can connect directly to social networking services with the help of libraries like Tweepy and InstaPy, or write a bot code and connect it to an API, just like the ones offered by YouTube Reddit, or Discord.

13. Creating a chatbot

These days, with the advent of ChatGPT and Bing Chat, the chatbot market is hot! Python makes it possible to build complex chatbots by integrating nltk with machine learning libraries. You can even add sound to your chatbot using the PyAudio and SpeechRecognition libraries and add speech-to-text functionality.

14. Connecting to the Internet of Things

With tools like Arduino and Raspberry Pi, you can build robots, home appliances, and small devices that connect to the Internet of Things and use the Python language. For example, MicroPython is an open-source project that greatly simplifies programming for microcontrollers. You can even set up your own  firewall or irrigation system using Python.

15. Use of other languages

Sometimes the project you have in mind cannot be completely written in Python. In this situation, it is not necessary to abandon Python completely and go for other languages; Rather, the flexibility of Python allows you to use their capabilities in your Python project with the help of special Python modules (extension modules) wherever you need to use another language such as C or C++.

Many technology companies and large and successful organizations in the world use Python language for their website backend development or data analysis. Here we get to know some of them:

Instagram

Instagram , the largest photo sharing application in the world with more than 2 billion daily active users, uses the Django framework, which is written in Python, for its backend, and the reason for this is the simplicity and popularity of Python.

Google

Google is the most used search engine in the world with a 93% share of the market. Google has been a fan of Python since the beginning, and its founders decided to “use Python wherever possible and C++ wherever necessary .” The ease of using Python is enough that Google’s first web crawler, which was written in Java, was later rewritten in Python to make it easier to use.

Spotify

Spotify, a music and podcast streaming platform, was launched in 2008 and has more than 450 million active users today. While Spotify’s website uses WordPress, its application is built with Python. 80% of Spotify services are based on Python and the rest are based on other languages ​​such as Java, C, and C++. Spotify also uses Python for data analysis and backend services.

Netflix

With more than 200 million members, Netflix is ​​the largest Internet television network in the world. Like Spotify, Netflix uses Python for data analysis. Additionally, it allows its software engineers to code in whatever language they are most comfortable with, and most Netflix programmers have preferred Python. According to Netflix engineers, Python’s standard library, its highly active and growing community, and the wide variety of available libraries make it possible for developers to solve any problem.

Reddit

The Reddit website has more than 400 million monthly active users and is the 10th most visited website in the world in 2023. Reddit originally used Lisp but was rewritten in Python six months after launch. The reason for this change was Python’s access to more diverse libraries and its flexibility in terms of development. When Reddit hires programmers, they tell them that everything they write must be in Python so that it’s easier to read and it’s easy to understand if the code they wrote is good or bad.

Python language has many fans among large companies and organizations. Other examples of prominent companies using Python include Facebook, NASA, Quora, Pinterest, YouTube, Dropbox, Amazon, Uber, Lyft, CIA, PayPal, Nokia, and IBM.

If you have no background in Python and want to start learning it from scratch, it usually takes three to six months to learn it; However, it takes several years to become an expert in this language.

If you have a background in the Java programming language and want to learn Python as a second language, it only takes a day or two to familiarize yourself with the Python environment and write your first “hello world” code. If you use interactive platforms like Educative or CodeCademy or freeCodeCamp, you can write very simple programs in Python in a few minutes.

On the other hand, if you plan to use Python in data science (for example, for data analysis or machine learning), it takes less time to learn, because for data science you only need a specific use of the language and an understanding Its basic principles do not take more than one to two months. According to 365datascience statistics, if you devote 5 hours of your time a day to learning Python, you can learn the fundamental principles required for data science analysis in Python within a month.

Fortunately, in order to be hired as a Python programmer, you don’t need full expertise in this field, and just learning Python, debugging, and familiarity with software development tools such as Git is enough; You will gain expertise along the way.

The best way to learn Python or any other programming language is to practice coding on a daily basis. Of course, that’s easy to say, because as soon as you start coding, you’re faced with big challenges, and all you have to do is drop a semicolon somewhere and you’ll get a whole bunch of error messages. That’s why you will need a guide to learn Python.

Although you’ll get the best guidance from face-to-face interactions with people familiar with Python, there are other ways to learn the language. For example, you can use free websites like w3school or geeksforgeeks or freecodecamp or online courses like The Complete Python Pro Bootcamp on the Udemy website and when you get a good understanding of this language, go to read a book like Automate the Boring Stuff with Python for a deeper knowledge of Get Python. Of course, reading a book is not an easy way to learn a programming language, and you can use online courses based on these books.

On the other hand, you can advance learning Python by running a project; For example, a project related to automation, building a web application, or even a machine learning model.

These days, learning Python with mobile applications has also become popular; Programs like SoloLearn or Datacamp provide you with a simple way to learn programming languages ​​and use an environment to run codes; However, you may need to get help from other guides as well.

From its humble beginnings as a small Christmas project, Python has taken a long and bumpy journey to become one of the most popular programming languages ​​in the world. Many of the key principles that led to the birth of Python, including simplicity and ease of understanding, still hold true for the language and will define its future development path.

Although Python is becoming more and more popular and has virtually taken over the field of data science, there are some challenges in its way. For example, Python’s presence in smartphones, which are more common these days than PCs, or multi-core processors, is minimal.