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Python for Development and Data Science

Python for Data Science and Development

Developed by Guido van Rossum, Python is currently very popular. This language, released in 1991, is part of the general-purpose languages category. As a result, it's pretty versatile and usable in several projects. This article will focus on what you can do with Python and its advantages and disadvantages. Further, we'll answer some of the most frequent questions about this programming language. Let's go!

Python for Software 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.

Python for Data Science

 Python for Data Analysis & Visualization: Data is an essential aspect of any business. Moreover, proper analysis and visualization are key to using that data correctly. 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.

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 pytest and unittest testing frameworks, and the doctest module.

What are the Pros of Python?

1. Open-Source. Python is open-source, which makes it free to use and distribute. You can download the source code, make changes, and distribute your version of it.

2. Easy to Learn. Python’s simplified syntax makes it friendly for beginners. Also, there are a lot of tutorials and documentation available. Given it's free to use, countless libraries and packages are available to help developers.

3. Community. Over the last three decades, Python has amassed many users. This large community is all about collaboration and sharing their experience. Developers at all skill levels are happy to provide tutorials, guides, and documentation. There are almost 2 million Python-related posts in Stack Overflow alone!

4. Versatility. Given that Python is a general-purpose language, it is incredibly versatile. As mentioned in the previous section, it can be used for almost any application in any industry or field.

5. Independent. You can develop and execute Python programs in various operating system platforms, including Windows, Linux, and Macintosh.

6. Libraries. Python libraries and frameworks are not only abundant, but they are also quite good, due mainly to its incredible community! We have already mentioned the Django and Flask frameworks. Yet, there are also CherryPy and FastAPI.

7. IoT-aligned. The Internet of Things (IoT) has grown steadily over the last few years. Further, it’s expected to grow even more. Given its versatility, many IoT devices use Python. We have already mentioned RaspberryPi; other examples include Intel Edison and Arduino.

8. Academic Use. Given its versatility and widespread use, Python has become the go-to programming language in academics. This guarantees the language will remain popular for the time being.

What are the Cons of Python?

1. Speed. Python is slow compared to languages such as C++ or Java. This is due to its dependence on an interpreter instead of a compiler.

2. Mobile. The language is also seen as weak in mobile development. That’s why it’s rarely used for projects in this area.

3. Database. Python is a robust programming language. But, it's underdeveloped in database access, particularly compared to technologies such as JDBC and ODBC.

4. 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.

What is the Python Data Class Module?

Python introduced the Data Class Module in its 3.7 version back in 2017. It allows the creation of structured classes specifically for storing data. This means you can define classes with less code and more functionality.

What is the difference between Python and Java?

When discussing Python vs. Java, the two programming languages have quite a few differences. Let’s look at them below.

Type. Python is an interpretive language. This means it can easily convert human-readable code into machine-readable code. Meanwhile, Java is a compiled language. It 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. Moreover, statically-typed languages like Java need you to declare data types for your variables.

 Syntax. Python’s syntax is simple and easy to understand, while Java’s is more complex.

 Performance. Typing affects the performance of each language. As Python compiles the code at runtime, it is slower and less agile. Meanwhile, as Java compiles in advance, code execution is faster.

 Speed. As Python is simple and easy to read, development is quite fast. Regarding Java, projects with this programming language take longer and may need a larger team.

 Use. We’ve seen that Python has many applications, and so does Java. Yet, Java is particularly popular for Android and web applications. Meanwhile, Python is preferred by mathematicians and physicists in the Artificial Intelligence field.

Why is Python used 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.

How is Python used in Data Analysis?

It is pretty easy to create and manage data structures with Python. For this, there are 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. Let’s take a closer look at these below.

 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.


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. Further, we hope you learn what makes it a great language to work with!