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, 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

• ‍Web Development: Python is often used for backend coding, with popular frameworks including Django and Flask.‍
• Game Development: Python is also used to develop interactive games with the help of Pygame modules to add functionality. ‍
• Desktop GUI: You can also develop Graphical User Interfaces (GUI) apps for desktop environments using Python.‍
• Embedded Applications: Since Python is related to the C programming language, you can also use it to create Embedded C software for embedded apps.

Uses of Python for Data Science

• Data Analysis & Visualization: There are quite a few Python libraries available for data analysis and visualization like Pandas, Seaborn, and Matplotlib.‍
• Machine Learning: Python focuses on code readability, which makes it perfect for Machine Learning with Keras, TensorFlow and Scikit-learn.‍
• Web Scraping:  There are plenty of tools for web scraping with Python, such as BeautifulSoup library and the Scrapy framework.

Uses of Python for Workflow Optimization

• Task Automation: Due to its simple syntax, you can use Python to automate repetitive tasks and processes, like converting files or remove duplicates.‍
• Software Testing: You can also use Python to automate testing and save lots of time, with testing tools inclikelude the pytest and unittest frameworks.

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. 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 over the last few years, and 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!