Developed by Guido van Rossum, Python is currently very popular.
This language, released in 1991, is part of the general-purpose languages category, making it pretty versatile and usable.
This article will focus on what you can do with Python and its advantages and disadvantages.
We'll also answer some of the most frequent questions about this programming language. Let's go!
What are the Main Uses of Python?
Uses of Python for Development
- Python for Web Development: Python is often used to code the backend of websites or web apps. Popular frameworks you can use with Python to develop web apps include Django and Flask.
- Python for Game Development: Python is also used to develop interactive games with the help of Pygame modules to add functionality. You can also use the Arcade and Pyglet libraries to create games with this language. For example, Disney’s Toontown Online was built using Python.
- Python for Desktop GUI: You can also develop Graphical User Interfaces (GUI) apps for desktop environments using Python. You can use various libraries (like PySimpleGUI) and frameworks (such as Kivy). Moreover, you can also use toolkits like Tcl/Tk with the tkinter interface.
- Python for Embedded Applications: Since Python is related to the C programming language, you can also use it to create Embedded C software for embedded apps. Raspberry Pi, a single-board functional computer designed to help young people learn about computing and digital technologies, is an excellent example of a Python-embedded application.
Uses of Python for Data Science
- Python for Data Analysis & Visualization: Data is an essential aspect of any business and proper analysis and visualization are key! There are quite a few Python libraries available for data analysis and visualization. These encompass libraries like Pandas, Seaborn, and Matplotlib.
- Python for Machine Learning: Python focuses on code readability, which makes it perfect for Machine Learning. Not to mention it has many libraries devoted to this field. Some of these libraries include Keras, TensorFlow and Scikit-learn.
- Python for Web Scraping: Another great use of Python in data is web scraping. This process collects web data with an automated tool often called a “crawler.” There are plenty of tools for web scraping with Python. For instance, there is the BeautifulSoup library and the Scrapy framework.
Uses of Python for Workflow Optimization
- Python for Task Automation: Doing the same task over and over again can quickly become tedious. Due to its simple syntax, you can use Python code to help you automate repetitive tasks and processes. For instance, to convert files or remove duplicates.
- Python for Software Testing: You can also use Python to automate testing for new products or features and save lots of time. Some of Python’s testing tools include the
pytestandunittesttesting frameworks and thedoctestmodule.
Pros and Cons of Python
Pros of Python
- Open Source: Since it's open source, Python is free to use and distribute. You can download its source code, make changes and distribute your version of it.
- Easy to Learn: Python’s simplified syntax makes it friendly for beginners. Given it's free to use, countless tutorials, libraries and packages are available to help developers.
- Community: Over the last three decades, Python has amassed a large community that fosters collaboration and sharing their experiences. Developers at all skill levels are happy to provide tutorials, guides and documentation. There are over 2 million Python-related posts in Stack Overflow alone!
- Versatility: Given that Python is a general-purpose language, it 's incredibly versatile and can be used for almost any application in any industry or field.
- Independent: You can develop and execute Python programs in Windows, Linux and Mac operating system
- Libraries: Python libraries and frameworks are not only abundant, but they are also quite good! We have already mentioned the Django and Flask frameworks, yet there's also CherryPy and FastAPI.
- IoT-aligned: The Internet of Things (IoT) has grown steadily over the last few years. iven its versatility, many IoT devices use Python's RaspberryPi;, Intel Edison and Arduino, among others.
Cons of Python
- Speed: Python is slow compared to languages such as C++ or Java due to its dependence on an interpreter instead of a compiler.
- Mobile: The language is also seen as weak in mobile development, making it rarely used for projects in this area.
- Database: Python is a robust programming language but it's underdeveloped in database access, particularly compared to technologies such as JDBC and ODBC.
- Memory. Python is not the top choice for tasks that are memory intensive since it uses large amounts of data. You might want to choose a different language if you are focused on memory optimization.
What jobs can I do with Python?
Given how versatile Python is, developers can specialize in various areas.
Some popular spots include game development, big data, smart devices and web testing.
When it comes to job profiles, these not only include software and development.
Further, it encompasses research analysis, machine learning engineering and data science.
Applications of Python
Python vs Java
When discussing Python vs. Java, the two programming languages have quite a few differences.
- Type: Python is an interpretive language that can easily convert human-readable code into machine-readable code. Meanwhile, Java is a compiled language that translates the source code into machine code before running it.
- Typing: Python is dynamically typed, while Java is statically-typed. The former performs type-checking at runtime, while the latter does so when compiling. Statically-typed languages like Java also need you to declare data types for your variables.
- Syntax: While Python’s syntax is simple and easy to understand, Java’s is more complex.
- Performance: As Python compiles the code at runtime, it's slower and less agile. Contrariwise, as Java compiles in advance, code execution is faster.
- Speed: As Python is simple and easy to read, development is quite fast. Java projects take longer and may need a larger team.
- Use. While Java is particularly popular for Android and web applications, Python is preferred by mathematicians and physicists in the AI field.
Python uses for Machine Learning
As we’ve discussed above, Python has countless libraries and frameworks.
These elements make the coding process more manageable. Further, there are quite a few devoted to Machine Learning.
That's why Python is rather popular in this field. Moreover, Python is platform-independent, which means it can run on any platform.
As a result, the language is simple and consistent. Also, it has outstanding online support and great visualization tools.
Other reasons for its use in ML include flexible integrations, fast code tests and readability.
Python uses for Data Analysis
Python eases creating and managing data structures with libraries such as pandas, BeautifulSoup, Seaborn and Matplotlib.
So, how is Python used in Data Analysis? Well, through data mining, data processing and modeling and data visualization.
- Python for Data Mining: We’ve already covered a bit of data mining when we mentioned that Python could be used for web scraping. You can use tools such as BeautifulSoup or Scrapy to collect data from the web or APIs.
- Python for Data Processing: NumPy and pandas are the go-to libraries for Python data processing and modeling. NumPy helps you arrange big data sets. Further, pandas structure your data in series and data frames.
- Python for Data Visualization: The other two libraries we mentioned, Matplotlib and seaborn, are great for data visualization. They can convert your numbers into graphics, pie charts, heat maps and graphs and charts.
Conclusion
Python is an extremely popular programming language and rightfully so. It is easy to use and learn and used in various projects and its resources are endless.
We hope this article has given you a clear rundown of what you can do with Python. We also hope you learn what makes it a great language to work with!
