The Python programming language is increasingly popular for web development, enterprise solutions, SaaS technologies, and system administration.
One of the reasons for this popularity is that both the Python community and commercial organizations keep creating libraries, frameworks, and software development kits (SDKs) for smoothly integrating Python applications with various third-party services. Thanks to these integration capabilities, developers can quickly and easily create solutions of any complexity in Python.
In this article, we explore the most common use cases for Python, list its pros and cons, and share the most commonly used Python tools for integration with third-party programs. This article will be helpful for development teams that are building software in Python and want to know more about integration capabilities.
Python is a high-level interpreted programming language that emphasizes code readability using significant indentation. At the time of writing, Python was the most popular programming language according to the TIOBE Index and the PYPL Index.
One of the reasons for such popularity is the wide number of Python use cases. You can apply this programming language for various purposes, including:
1. Web application development. Python allows developers to create websites and applications according to several different programming paradigms. For instance, Python is suitable for both object-oriented programming and functional programming. Many famous apps including Instagram and Spotify have been developed using Python.
2. Data analysis. Python is convenient to use for data mining, processing, modeling, and visualization. For example, it offers NumPy, which is an array processing package for scientific computing. NumPy provides high-performance multidimensional array objects as well as tools for working with these arrays.
4. Artificial intelligence (AI) and machine learning (ML). Python offers various packages including NumPy, SciPy, and Matplotlib that are tailored for developing AI and ML solutions. At Apriorit, we often leverage the capabilities of this language to develop AI-based projects. For example, we’ve evaluated various Python tools and models for speech recognition. Our developers have also explored how to create smart chatbots using ready-to-use Python libraries.
5. System administration and DevOps. You can successfully use Python for infrastructure automation and orchestration. No wonder popular automation tools like Saltstack and Ansible are written in Python. When applied for system administration tasks, this programming language can help you:
- Automate DevOps lifecycle management as well as your continuous integration and continuous delivery (CI/CD) pipeline
- Modify, configure, and automate DevOps tools
- Automatically deploy applications from Dev to QA to Prod environments
6. Automation testing. Software testers and QA engineers often leverage tools and libraries like Pytest and the Robot framework that are offered by Python. At Apriorit, we’ve explored how Pywinauto – a set of libraries for automating Windows GUI testing with Python – can automate tests for Windows applications. Windows GUI automation with Python and Pywinauto has proven to be quite straightforward and can help you cover a huge number of Windows GUI technologies while providing easy access to user interfaces of tested applications.
7. Blockchain. Blockchain developers often prefer Python to other languages because of its simplicity. For example, there are no white spaces denoting code blocks, so developers don’t have to worry about using curly braces or keywords. Python allows you to simply correct errors and relaunch your application without recompiling code, which is a significant advantage for blockchain development.
8. Software prototyping. Python requires fewer lines of code than more verbose languages like Java or C++, which allows developers to rapidly and easily prove their idea for a full-scale solution.
The list of use cases for Python could go on and on. At Apriorit, we also apply Python for hooking Windows APIs. While API hooking practices help developers alter the behavior of an operating system or an application, detect malicious code, and build strong products, it’s usually done using compiled languages like C or C++. However, Python has also shown good results and proven a convenient language for these purposes.
Before choosing this programming language for your software, it’s essential to consider all of Python’s strengths and weaknesses. Let’s explore the pros and cons of Python, starting with the advantages.
1. Easy to learn. Python is a high-level programming language with a syntax similar to English, which makes it easy to understand Python code. It allows beginners to focus on the main concepts of the language and good coding practices rather than on the complexities of the language’s structure. This is a significant benefit for developers that haven’t worked with Python before and need to quickly learn it.
2. Quick to develop. Due to Python’s simplicity, a programmer can concentrate on solving a problem rather than spending time learning the language’s nuances and overcoming unobvious pitfalls. Since Python is an interpreted language, developers can quickly change and execute code without needing to compile and build it every time they need to introduce changes. Write less – do more could be a Python slogan.
3. Free to use. Python is developed under an OSI-approved open-source license, so you can freely use and distribute it, even for commercial purposes. The official Python website offers free downloads of Python versions for Windows, Linux/UNIX, macOS, and other operating systems. You can also use custom versions of Python or modify the language yourself.
4. Portable. Since Python is a platform-agnostic language, it allows you to write code once and then run it on different operating systems. You only have to remember about system-dependent features, like the functionality of a particular operating system (OS) library. Such portability is possible thanks to bytecode and the Python Virtual Machine (PVM) that serve as mediators between the developer and the actual CPU executing the program.
5. Offers numerous libraries. The Python Standard Library provides a large functionality set for system programming, database management, and more. Apart from it, you can leverage a bunch of third-party libraries that can be easily installed using the official Python package manager, called pip.
6. Compatible with other languages. Programs written in Python can be easily bound with programs written in other languages like C++, Rust, GoLang, and Java. For example, you can speed up your Python program by integrating libraries written in Rust.
However, despite all the advantages and mass adoption, Python isn’t a silver bullet for all programming problems and tasks. Let’s explore some major Python disadvantages.
1. Slow speed. Python might not be the best choice for a solution where speed is a crucial requirement. For example, Python is considered significantly slower than C/C++ and Java. This drawback can be explained by the fact that the language is interpreted, high-level, and dynamically typed. However, in a century of high technologies and affordable servers, the speed of a programming language doesn’t play such a big role, as a user won’t notice if an application loads in 0.001 seconds versus 0.01 seconds.
2. Not suitable for mobile development. Python is rarely used for creating mobile applications because it doesn’t have built-in mobile development capabilities. Developers tend to choose Java or Kotlin for native Android app development and Objective-C and Swift for iOS development. You can still create apps with Python using cross-platform frameworks like Kivy, PyQt, and Beeware, or with development environments for iOS and iPadOS like Pythonista and Pyto. However, the development process will be slower and more complicated than when using the languages mentioned above.
3. High memory consumption. Python’s RAM consumption is higher compared to C/C++ due to the flexibility of its data types. It’s also harder to control memory usage with Python. The reason is that Python objects come with a huge memory overhead and use much more memory than needed to store information. The more objects you create, the more overhead you get, preventing you from running your program and increasing expenses on additional hardware. This is why Python is not the best choice for solutions that should use a strictly limited amount of RAM.
4. Runtime errors. Python is dynamically typed, which means developers don’t have to declare a variable’s type and the same variable can change its type many times during program execution. This may lead to a large number of runtime errors. Therefore, Python applications must be properly tested and, ideally, have all functionality covered with strong unit tests.
One of the reasons developers choose to create products in Python, despite its downsides, is because of the numerous clients, libraries, and SDKs designed to easily integrate various third-party services into Python products. Such integration capabilities significantly simplify and accelerate the development process.
Let’s take a closer look at these capabilities and explore a few examples of Python tools for integration with third-party products.
How does Python integrate with other technologies?
When you’re developing any software, you need to ensure integration with third-party libraries, databases, and other IT products. To allow for integration with their services, software providers and communities offer public APIs that are usually implemented based on HTTP, SOAP, MQTT, or another protocol.
However, if you’re writing software in Python, you can leverage lots of special Python wrappers for third-party products’ APIs. Many commercial companies and developers from the Python community create such wrappers as an SDK or a client library that can be downloaded from an official Python repository like PyPi.
Let’s take a closer look at the most commonly used and helpful tools for integrating your Python software with third-party services.
Large software providers often write solutions in Python to help you quickly and easily integrate their products with your software. A few famous examples of such tools are:
- Google API client — a client library that simplifies access to Google Cloud APIs and reduces the amount of code needed to use Google services remotely
- Facebook API library — a Python wrapper for the Facebook Common API
- Dropbox SDK — a tool to simplify use of the Dropbox API
Talking about using Python for integration with third-party solutions, it’s worth mentioning McAfee products, as McAfee offers a bunch of libraries developed specifically for integration with different McAfee services.
At Apriorit, we have experience integrating McAfee solutions in our projects to ensure efficient work and save time. For example, we once used the OpenDXL TIE library to send data about malicious files (this data was stored in a knowledge database of the project under development) to the McAfee TIE server. Thus, McAfee TIE users received an opportunity to enrich their database with the newest information about malicious files. Thanks to the OpenDXL TIE library, our team was able to develop a project quite fast and with almost no issues along the way.
These are the most popular McAfee tools:
- OpenDXL is a library that makes it possible for third-party programs to interact with the McAfee Data Exchange Layer (DXL). The OpenDXL Python Client enables the development of applications that connect to the McAfee Data Exchange Layer messaging fabric to send and receive events as well as invoke and provide services.
- OpenDXL TIE is another McAfee library that works as a wrapper for OpenDXL but is designed to solve a more specific problem: interacting with the Threat Intelligence Exchange module of McAfee DXL. The McAfee Threat Intelligence Exchange (TIE) DXL Python client library provides a high-level wrapper for the TIE Data Exchange Layer (DXL) API. This library allows users to access features of TIE (manage reputations, determine where a file has been executed, etc.) without having to focus on lower-level details such as TIE-specific DXL topics and message formats.
- McAfee SIEM API wrapper is a module designed to provide an API wrapper for McAfee SIEM API principal components.
You can also easily integrate your Python product with the most commonly used relational databases such as MySQL, SQLite, and NoSQL databases like MongoDB.
- MySQL Connector is an easy-to-use tool for integrating products written in Python with MySQL, which allows for the conversion between Python and MySQL data types. MySQL Connector API is implemented using pure Python and doesn’t require any third-party libraries.
- SQLite3 is a module for integrating the SQLite database with Python. It provides a SQL interface compliant with the DB-API 2.0 specification and requires SQLite 3.7.15 or newer.
- PyMongo is a tool for working with MongoDB — one of the most popular NoSQL cross-platform and object-oriented databases. PyMongo provides a simple interface to maintain databases along with a library that comes with extensive and clear documentation.
The libraries we mentioned above are not the only ones supported by Python. As a language for creating database clients, Python can be integrated with all types of databases.
Most medium-sized applications don’t need to implement special SDKs, libraries, and API wrappers because they often have quite narrow functionality that is easy to work with using only standard Python tools. Instead of developing custom wrappers, they create a public API (usually a REST API) that provides access to their functionality.
However, there are products that use other protocols like SOAP and MQTT. To integrate your Python solution with such products, you need to use special clients. Let’s explore a few:
1. SOAP clients. SOAP is an XML-based communication protocol for exchanging structured information. Here are a few Python clients that interact with SOAP servers:
- Zeep is a well-maintained library that provides an easy-to-use programmatic interface for interacting with a SOAP server and offers a wide range of functionality.
- SUDS is a lightweight SOAP client. Note that it requires installation of different packages for different versions of Python.
- PySimpleSOAP is a simple and lightweight Python SOAP library for client and server web services interfaces. It’s designed to be as small and easy to use as possible.
2. HTTP clients. Here are a few of the most popular Python wrappers that work with the HTTP protocol:
- Urllib3 is a powerful HTTP client for Python. The library supports many critical features such as uploading files with multipart encoding and pooling connections that are missing from the standard Python libraries.
- Requests is the library built on top of urllib3. With a simple and user-friendly interface and limited functionality, this library is the best choice for small programs.
- Aiohttp is an asynchronous HTTP client/server for Python. Its API is designed to make the most of non-blocking network operations.
3. MQTT clients. MQTT is an OASIS standard messaging protocol for the Internet of Things (IoT). It’s designed as an extremely lightweight publish/subscribe messaging transport that is ideal for connecting remote devices with a small code footprint and minimal network bandwidth. Let’s explore a few examples of such clients:
- Paho-mqtt is one of the best Python MQTT open-source client libraries. It’s developed under the leadership of the Eclipse Foundation and implements versions 5.0, 3.1.1, and 3.1 of the MQTT protocol. The library provides a client class which enables applications to connect to an MQTT broker and publish messages, subscribe to topics, and receive published messages. It also provides helper functions to make publishing one-off messages to an MQTT server straightforward.
- HBMQTT is an open-source MQTT client and broker implementation built on top of the asyncio library, Python’s standard asynchronous I/O framework. HBMQTT provides a straightforward API based on coroutines, making it easy to write highly concurrent applications.
- Gmqtt is not as popular and well-developed as the previous two clients, since it’s an open-source tool created by an individual developer. Qmqtt supports the MQTT 5.0 protocol. The module is built on the asyncio library and requires Python 3.4 or newer.
The list of Python tools for integration with third-party solutions goes on and on. Above, we discussed only a few of the most popular. When choosing a tool, make sure to check which Python versions it supports.
Building solutions in Python often takes less time than doing so in other popular languages like C, C#, and Java, as developers can quickly understand Python syntax and need to write fewer lines of code. Another reason for Python’s popularity is that the Python community offers a variety of libraries containing pre-written code that can be reused in order to solve a particular problem. In addition, there are a vast number of existing Python clients, libraries, and SDKs that can help you easily integrate your Python-based software with third-party services, significantly accelerating product development.
At Apriorit, we have expert development teams experienced in using Python for developing web applications and other types of software, integrating third-party products, and creating custom APIs. Moreover, our tech specialists keep researching and exploring Python’s capabilities for automation testing, AI and ML development, and more.
Leverage Apriorit’s experience in Python development. Contact us to start building your efficient solution today!