Why Python is a Leading Programming Language?

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Python is the most widely used programming language in the world. According to Statista research, 25% of developers know how to code in Python. Web development, system operations, scientific modeling, server, and administrative tools are the main spheres where this language is implemented. What are the key characteristics of Python that make it popular in IT? In general, there are 7 reasons that explain this fact. So, let’s find out more about Python and its advantages in this article: 


  • Simple in use

Python doesn’t have a lengthy code like Java that makes it easy-to-learn for beginners. Also, it’s not necessary to know about pointers as C and C+, and debugging doesn’t require much time. The other advantage of Python is speed, reliability, and efficiency. It means that it’s possible to create python development services without any problems.


  • Machine learning algorithms

Machine learning is a method used in data analysis for recognizing patterns and finding conclusions with minimal human intervention. Its algorithms can be easily applied to Python modules like SciPy, Numpy, and many others. The additional benefits of using this programming language in machine learning are effective data capacity, scientific computing, and data processing.


  • Big Data and its implementation

Python includes Big Data and cloud computing solutions in their work. It’s a good combination for integrating data analysis and web apps or production databases with statistical code. Other than that, Python’s libraries include in their packages such features as statistical analysis, visualization, numerical computing, etc. Scalability is additional functionality that this programming language provides to Big Data technologies making them work faster.


  • Wide range of libraries and frameworks

Python has various libraries that could be adjusted to different needs of app development. For example, Flask and Django are good for backend web development. Pandas, TensorFlow, SciPy, Keras, NumPy, and SciKit-Learn are useful for machine learning and data science training.

. Python developers use cloud media services like Encoding.com that ensure cross-platform support. 


  • Python implication in Data Science

Python defines CSV output that allows developers to analyze data in a spreadsheet. This feature makes it beneficial for data science. Pandas is a Python Data Analysis Library that is used for importing datasets from Excel to processing sets aimed to conduct time-series analysis. NumPy offers tools for analyzing data science, while Statsmodels enables statistical analysis. 


  • Reliability and Efficiency

Developers can work with Python implementing it to any type of environment without losing performance and scalability. The other feature of this programming language is versatility, and it’s possible to work on several domains at the same time. Web development, mobile and desktop apps, hardware and others are not the only processes that could be coded in Python as artificial intelligence and scientific computing are the additional areas where developers can use it. 


  • A large community of Python developers

Python is ranked as the third-largest community of developers on the opened pull and site-based requests. The explanation of why this programming language gains traction is its accessibility. Most beginners don’t have any hurdles in finding suitable resources for learning Python or asking the question from professional Python developers. There is plenty of tutorials, tech documentation, guides, etc. that help users out with any information about Python and its working principles. 

Now, you’re equipped with knowledge about Python and its advantages. The use of this programming language shouldn’t be underestimated nowadays as it’s used in Big Data, machine learning, data science, cloud computing, scientific modeling, and other fields. Even Google, a famous tech company, implements Python for creating its products since 2006. That’s why this programming language has a bright future thanks to simple algorithms and high data scalability. 

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