The programming language Python. What projects can be implemented in Python?
Python is a very versatile programming language with a large number of users who utilize it for a wide range of tasks. If you’ve mastered the basics of Python and want to build something practical and able to work, it’s important to understand the first step to take.
What do developers do with Python?
What are the possible applications of Python and for what tasks? Let’s take a look at some of the most famous examples in the industry. To begin with, let’s go through the leading IT industry companies that use Python to implement their solutions and tasks.
Google has used Python since the beginning, and today it ranks as the leading giant in server-side software languages. Guido van Rossum even has been working in Python for several years, observing how the language develops and what problems a developer faces while using it.
Instagram loves Python for its simplicity. The service is known for the largest deployment of the Django web framework, which is written entirely in Python.
Spotify uses Python language because of its data analysis services and backend. According to the development team, Python’s ease of use allows getting high development speeds. Spotify runs tons of analyzes to collect recommendations for its users, so they need something that can do the job quickly. Python is their solution!
Python is the perfect tool for many tasks
From the simplest scripts for solving simple problems and web development up to working with scientific data, machine learning, and more, Python applications have no boundaries for use. Let’s look at a few to help you to identify Python as your preferred language for development.
1. Routine tasks automation
This language allows you to automate routine and common tasks such as updating spreadsheets, parsing data from various web sources, renaming files on your computer, creating electronic dictionaries, scraping sites, working with files, and creating objects and classes.
2. Database automation.
Today, a lot of people already know about Bitcoin and other cryptocurrencies. Since December 2017, when the Bitcoin rate climbed to the $ 20,000 mark, many cryptocurrencies and digital tokens have become a talking point for tens of millions of people around the world. Prices change every second depending on various factors in trading markets around the world. To determine the right moment to buy and sell currencies, shares, or products, you must always have an up-to-date database of prices, markets and receive fast signals, and this can be easily implemented using Python.
First, you need to understand that databases are divided into:
If you want to get rich by trading currencies, stocks, or cryptocurrencies and want to know when to take the next step, then you need to have an idea of the best prices for them.
Well-structured databases that run smoothly allow you to solve many problems without increasing the investment of time, money, and additional human resources. Searching for and obtaining the necessary information in automated databases does not require special efforts
With the help of database automation, you can always get access to up-to-date information about the market situation, prices, trade, population, system, or business functioning. At the same time, the use of an automated database management system will significantly reduce peak server loads by distributing their resources more evenly. As a result, the cost of electricity and equipment maintenance will decrease.
Stages of database automation:
- Study and program analysis of the subject area – data types and processes.
- System design – the creation of the necessary links and entities.
- Writing database applications – functions, views, procedures.
- Creation of administration modules – separate access to information, automatic execution of various operations, creating backups.
- Setting up the ability to import information into the database from various sources – networks, the Internet, XML Excel files, Word, etc.
- Automation of the monitoring, analysis, and reporting system.
Despite its apparent simplicity, data processing automation and access to information are rather complex and responsible tasks – the entire work of a scientific project or application depends on the uninterrupted functioning of databases.
3. Encryption and Cryptography in Python
Encryption is a way of obscuring the original meaning of a message or other document, thus corrupting its original content. Often, coding and encryption are mistaken for the same thing, forgetting it is enough to know the replacement rule to recover an encrypted message, while a key to the cipher is required to decrypt an already encrypted message, in addition to knowledge of the encryption rules. In this case, a key means a specific secret state of the parameters of the encryption and decryption algorithms. You can encrypt not only texts but also various data – from database files and word processors to image files.
From the Middle Ages to the present day, the need to encrypt military, diplomatic, and government documents stimulated the development of cryptography. Today, the need for tools that ensure the security of information exchange has grown exponentially. Cryptography makes it possible to transform the original information in such a way that its recovery is possible only with the key. A cryptographic system is a family of T transformations of the plain text. The components of this family are indexed or denoted by the symbol k; parameter k is the key. Keyspace K is the set of possible key values. Usually, a key is a sequential series of letters of the alphabet.
There are not many standard library tools in Python that work with encryption. However, we do have hashing libraries at our disposal. If you need to secure hashes or the message digest algorithm, then the hashlib module of the Python standard library is perfect for this. It includes secure FIPS hashing algorithms such as SHA1, SHA224, SHA256, SHA384, and SHA512 and MD5. Python also supports the adler32 and crc32 hashing functions, but these are contained in the Zlib module. One of the most popular uses for hashing is storing the hash of the password instead of the password itself. Of course, the hash must be good, otherwise, it can be decrypted.
Another popular use of hashing is to hash a file, with the following sending it and its hash separately. The recipient of the file can run a hash on the file to verify that the file matches the sent hash. If so, then no one changed the file when it was sent.
The PyCrypto package is probably the best known third-party cryptography package for Python. Unfortunately, its revision stopped in 2012. However, it continues to be released for different versions of Python, you can get PyCrypto for version 3.5 inclusive
The cryptography package aims to be “the cryptographer for the people”, just as the requests library is “HTTP for the people”. But the thing is that you need to develop simple cryptographic recipes that are both secure and easy to use. If necessary, you can move on to low-level cryptographic primitives that only need to know what you are doing, otherwise, you will create something useless in the context of security.
4. Creating a calculator
This simple implementation of Python’s capabilities is an excellent experience in the world of GUI programming. Building backend services is an important part of the deployment, but there may be a need for a frontend to consider. Creating apps that users can easily use is paramount
If you are interested in UX \ UI design, then you will like working in Python with UX \ UI. You will probably be working with the Tkinter module, the standard graphical user interface package that is shipped with Python.
The Tkinter module is a wrapper around Tcl / Tk, a combination of the Tcl scripting language and an extension of the Tk graphical user interface framework. If you have Python installed, you already have Tkinter ready to use. You need to make a simple call before starting. Once installed, you can start building your first GUI calculator in Python.
5. Parsing Twitter or Facebook data
Thanks to the Internet, and the Internet of Things (IoT), we have access to a huge amount of data that we could not have dreamed of a few years ago. Statistics and analytics are a huge part of any data-driven business. What do people talk about? What excites and delights them? What behavioral patterns are visible in their actions?
Twitter and Facebook are great places to get answers to these questions. If you are interested in data mining, then Twitter and Facebook data mining is a great way to try your Python skills to answer questions about the world around you.
Using Python to parse Twitter and Facebook will allow you to pull data from them and analyze user sentiment in a docker environment. You need to register your app with the API to fetch data from Twitter and Facebook and to access the streaming API data.
You can use Tweepy to filter the tweets you want to pull, TextBlob to count the mood of those tweets, Elasticsearch to analyze the content of those tweets, and Kibana to display the results. Data mining projects allow you to tackle other projects that use Python for word processing and speech recognition.
6. Building a blog with Flask
Many people today have their blog and there is nothing wrong with having your blog online. With the development of Twitter and Instagram, microblogging has become extremely popular. With the Flask web framework, you can create your microblog.
After working through the available materials on the web, you will gain an understanding of the Flask web framework. Flask allows you to create a fully functional web application in Python.
You need to know the Flask basics to get started with web development. You can also switch to Django and build larger-scale web applications in Python.
7. Your blockchain creation.
Remember that a blockchain is an immutable, sequential chain of records, each part of this chain is called a block. They can contain transactions, files, or whatever kind of data you like. However, the important point is that they are linked together by hashes.
Although blockchain is primarily developed as financial technology, it can be applied in many other areas. Blockchains can be used in almost all transactions, from real estate transactions to the transmission of medical reports and court decisions.
You can build your blockchain to process any kind of transaction on a distributed network basis. You can use HTTP clients and the requests library for work. You can use HTTP requests and interact with your blockchain on the Internet after installing the Flask web framework.
In general, you should be more or less fluent in reading and writing the basics of Python to build a blockchain, along with understanding how HTTP requests work.
What do we need? Make sure you have Python 3.6+ (as well as pip) installed. You will also need to install Flask and the awesome Requests library. You will also need an HTTP client like Postman or cURL
First, we will create a blockchain class whose constructor creates an initial blank sheet (for storing our blockchain) and another one for storing transactions. Our Blockchain class is responsible for managing the chain. It will store transactions and will also have several helper methods for injecting new blocks into the chain. Each block contains an index, timestamp (Unix time), transaction list, proof, and the hash of the previous block.
From this point on, the understanding of the chain should be separate – each new block contains the hash of the previous block inside. This is fundamentally important since this ensures the immutability of the blockchain: if an attacker breaks the previous block, then all other blocks will contain incorrect hashes. We will need a way to inject transactions into the block. The new_transaction () method can be used to handle this, and it’s pretty straightforward. After new_transaction () inserts the transaction into the list, it will return the index of the block into which the transaction should be entered – namely the next one. In the future, this will be useful for the user submitting the transaction. After we have received the blockchain sample, we need to plant a genesis block into it – the first block without predecessors. We also need to put a “proof” into our genesis block, which is the result of mining (proof of work done). In addition to creating a genesis block in the constructor, we will also set up methods for new_block (), new_transaction (), and hash (). The Proof of Work (PoW) algorithm is how new blocks are created or mined on the blockchain. The goal of PoW is to find a number that solves a problem. The number should be hard to find, but easy to confirm (in terms of calculations) by anyone on the Internet. This is the main task of the algorithm.
The mining endpoint is the part where the magic happens and it’s simple!
To do this, you need to do three things:
- Calculate PoW;
- Reward the miner by adding a transaction giving us 1 coin;
- Build the next block, inserting it into the chain
Thus, the main skeleton of our blockchain is built.
Blockchain is not just for cryptocurrency fans. By building one yourself, you can easily find creative ways to implement this technology in your data science or business area of interest. Many enthusiasts believe that blockchain will radically change the way we think about the economy, government, transactions, and accounts!
8. Processing data from the Twitter feed
For those interested in web development, it will be useful to learn how to create quite powerful web applications in a week to start your mega project.
Bob Belderbros shared a case study where he created the 40th PyBites Code Challenge, in which participants needed to build a simple web application to better navigate the Daily Python Tip Twitter feed.
In this case, it is recommended to use the Bottle web framework instead of Flask,. It is known for being a lightly dependent solution for quick application building. Since it was designed to be lightweight and easy to use, you should be able to create your application in almost a few days.
In this case, you can use the Tweepy module to download data from the Twitter API and save the data to an SQLAlchemy or Peewee database.
9. Making games in PyGames
Python can be used to write a variety of logic and arcade games, adventure games, and puzzles that will take only a few days to develop. To create a classic game such as ping-pong or Tetris, you need to have a better understanding of Python.
The main thing in games is the movement of pixels on the screen and the noise it makes. Almost all video games have these elements.
Pygame is a Python language framework for programming games. It is built on top of SDL and has everything you need:
- good community
- open source
- cross-platform base
- quality documentation
- many examples of games
- ease of learning.
Many functions in a game can be implemented through the Pygame library, which makes game development much easier. It includes pretty much everything you need to get started developing your game. Pygame is completely free and open-source. It includes computer graphics libraries and audio libraries that you can use to add interactive functionality to your game application. We have access to the possibilities for creating more than a dozen types of games that can be created using the Pygame library.
The main loop of the game runs and refreshes the screen at fixed intervals. These are called frame rates and determine how smooth the movement is. Games typically refresh the screen 30-60 times per second. If the frequency is lower, it will seem that objects on the screen are twitching. There are three main operations Inside the main loop: handling events, updating the game state, and drawing the current state to the screen.
Events in a game consist of everything that happens outside of the control of the game code but relate to the execution of the game. For example, if in a game the player presses the left arrow key, the game needs to move the object to the left. Standard events are key presses (and releases), mouse movements, mouse clicks (especially in menus), and timer events (for example, a special effect can take 10 seconds).
The heart of any game is its state: everything that it tracks and draws on the screen. There is also an auxiliary state that allows you to control the game:
- Is the menu displayed now?
- Is the game over?
- Did the player win?
The game needs to display its state on the screen, including drawing geometric shapes, images, and text.
Most games simulate a physical environment. Objects have a very rough system of solid-state physics (if you can call it that) in many games.
More complex games can use more sophisticated and realistic physics systems (especially 3D games). It is also worth noting there is almost no physics in some games, for example, in card games, and this is entirely normal.
We fight against computer opponents in many games – the simplest artificial intelligence. They often behave in the game world as if they have a mind. For example, enemies are chasing the player and are aware of his location. However, the AI in games is often very simple and just follows simple (or complex) rules to produce pseudo-sensible results.
Sound reproduction is another important aspect of gaming. In general, there are two types of sound: background music and sound effects. Background music is just music playing constantly in the background. It is not used in some games, or it may change at every level in others.
If you prefer gaming novels, then you still have tons of tools to create something cool and big in Python. The language is very easy to write, making it an ideal environment for developing interactive games. If you want to take your storytelling game to the next level, you can use the RenPy engine to add sounds and imagery to your game, creating a fully immersive visual storytelling novel.
10. Machine learning and data science
Machine learning and data science is the next step for creating self-learning machines and, as a result, creating a full-fledged artificial intelligence. However, it is easy to get confused in this area, as it is constantly evolving and changing.
The first component of data science, without which the entire further process is impossible, is the data and decisions themselves: how to collect, store and process them, as well as how to extract useful information from the general data set. It is precisely data cleansing and bringing them to the desired form that specialists devote up to 80% of their working time.
An important part of data science is how to deal with data for which standard storage and processing methods are not suitable due to their huge volume or variety – the so-called big data. Therefore, big data is a part of the work in data science. At the same time, data analysts do not always have to work with big data in practice. Depending on the tasks, small ones can be useful.
Machine learning (ML) is a branch of science and technology that solves the problem of teaching computers. This is understood as the transfer to hardware and software systems of some highly limited set of knowledge with the possibility of their subsequent accumulation and interpretation. This context doesn’t presuppose full-fledged training comparable to human´s one. This is not the thing called “with deep understanding”, but rather, as a result, the computer acquires machine role knowledge, which is closer to by role. If this circumstance is not stipulated in advance, then there are no gains to get down to “pedagogy of robots”, we can already meet this term. Machine knowledge today does not allow making truly intelligent decisions comparable to human capabilities.
The current practical need for ML appeared since today the variety of inputs and possible solutions is becoming too large for traditional pre programmed systems.
Every year the need for studying big data is growing for companies as well as for active enthusiasts. Large companies such as Google, Amazon, Microsoft are increasingly using data mining tools such as the R programming language or Python libraries. Every year, the performance of our computers is growing, which means that the previously inaccessible boundaries of cognition are again “shifting to the right”. There is a huge space for studying big data, which is the main reason for the “big data science” creation, the study of which became possible mainly thanks to the application of the previously described machine learning algorithms, though we could test them half a century later.
ML is implemented on computational and statistical principles, combining a variety of approaches, including probability theory, statistics, logic, computational optimization, search methods, reinforcement learning, control theory, and possibly some others. The scope of ML application currently covers a wide range of applications, including processing all possible types of data (text, video, audio), forecasting, pattern recognition, data mining, expert systems, robotics, and even games.
Along with the particular importance for all mankind to study big data, we also should mention a relative simplicity in independent study and application of the “result” received. Today there are a huge amount of resources to solve the classification problem. The simplest learning machine looks like two lines of code:
clf = RandomForestClassifier ()
clf.fit (X, y)
So we have created the simplest machine capable of predicting (or classifying) the values of arguments according to their characteristics.
You need to familiarize yourself with some basic machine learning algorithms and basic Python libraries that will help you to make predictions and process scientific data and to find solutions to today’s problems of humanity, science, and business.
A simple implementation of a data classification task using Python tools is implemented in three steps:
- Create a training sample;
- Try to train the computer on randomly selected parameters and classes corresponding to them;
- We calculate the quality of the realized training.
Machine learning and scientific data allows you to use Python and specialized libraries to be able to teach a machine to analyze data, create visualizations, recognize speech and faces, photographs, find the results you need, all this you can find in a universal Python.
What is (most likely) not worth doing in Python
Python is an extremely versatile language that you can do a ton of things with. But you cannot literally do everything. There are certain areas that Python is not designed for.
In terms of an interpreted language, Python has problems with interoperability with low-level devices such as device drivers. For example, you will have problems if you want to write an operating system in Python only. You’d better link it with C or C ++ for low-level applications.
However, even this may not be a problem for long. People are working on projects that extend Python’s usability for low-level interactions as proof of Python’s flexibility. MicroPython is just one such project that develops low-level Python capabilities.
What if your idea isn’t on this list?
This list can hardly be called full. There are tons of other tools and applications that you can create in Python that we haven’t covered in this article. Don’t feel like your ideas should be limited just to this list.
Software and applications development in Python.
Python is great for the fast development of IT projects. The secret to speed is in syntax that is as close to natural English as possible. The speed is influenced by a large number of free libraries and frameworks, as well as minimalistic micro-frameworks such as Flask. They are designed for different needs, have a large and friendly community, and attract with their simplicity and flexibility of syntactic constructions.
But the high speed of development in Python is supported with relatively low complexity of the project (small and medium e-Commerce, CRM, simple integrations).
If the development is carried out for enterprise projects, then all work takes approximately the same time, regardless of the technology stack used. It doesn’t matter if you write in Java, PHP, or Python, the total number of hours will not differ significantly. At the same time, time can be distributed in different ways for different types of work. With some stack, more time will be needed for refactoring, with another – for MVP, with a third – for tests, etc. For example, to work quickly in Python, you need to think over the architecture and prepare the environment correctly. Besides, one cannot judge the work complexity by the number of code lines and the speed of writing. The minimum amount of code means its high quality, and for a developer to be able to put a task in three lines, he must think carefully about how to do it, in which part of the program to place the code, etc.
Easy integration with other technologies and software is a beneficial advantage for Python development. Today, any development product requires a large number of integrations: with a variety of information systems of the company itself and its counterparties, with banking, marketing IS, and many others. Python is easy to integrate and can manage various software components, including those developed in another language. Universal for any platform
Scalability and the ability to add new features are the second major benefits of Python. This advantage does not arise by itself, it requires a well-thought-out design architecture and keeping the code clean. We can say that it is easier to write scalable code in Python, with its simple and straight syntax than in most other languages.
The speed of code execution by applications written in Python is the most painful topic for “pythonists”. Python is considered a rather slow programming language: this is due to the peculiarities of the language interpreter structure and its dynamic nature. Developers either rewrite the “slow” piece of code in C or Cython (a Python-to-C translator) to improve performance or use PyPy, an alternative interpreter for the Python programming language (its name stands for Python written in Python).
Typical development tasks that are successfully solved in Python:
- simple web applications and interactive websites;
- corporate website (catalog, online store);
- systems for corporate training and hackathons;
- image recognition systems (photos, images, faces);
- document recognition systems (OCR, machine vision);
- management of printing services with complex routes;
- asynchronous modules for existing systems (transport modules, payment gateways), applications for processing scientific data;
- blockchain applications;
The microservices in the development of which Python is most commonly used:
- for machine learning;
- for processing objects with a request and a response;
- when updating online stores based on CMS systems developed a long time ago.