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Data Science: 9 Reasons Why Python Is The Language Of Choice For Data Scientists

With its powerful data analysis capabilities, Python is fast becoming the language of choice for data scientists. This article explores the reasons why Python is a good choice for data scientists.

Python is an interpreted, dynamically-typed language with a precise and efficient syntax. Python has a good REPL and new modules can be explored from the REPL with dir() and docstrings. That’s one reason to prefer Python over C, C++, or Java

Python is a programming language that is open source, versatile, easy to learn and has a large community of developers.

Python is considered the language of choice for data scientists because it is simple enough to be scalable and it is easy for beginners to pick up.

Here are some other reasons why Python is the language of choice for Data Scientists.

9 Reasons Why Data Scientists Prefer Python 

1. Less Is More

“Simple is better than complex,” wrote Tim Peters in “The Zen of Python,” a collection of 20 software principles that have influenced the design of the Python programming language. Python is known for making programs run with as few lines of code as possible. It automatically identifies and associates data types and follows an indentation-based nesting structure. Overall, the language is easy to use, and it takes less time to program a solution in Python. Some people refer to Python as “the Swiss Army knife of programming.” Python is versatile and simple, but R is a specialized tool designed specifically for data analysis.

2. One Language for Everything

As a general-purpose programming language, Python is universal. It is a fast but powerful tool with many possibilities. With Python, you can build your machine learning models, web applications, and everything else you need in a single language. This simplifies your project and saves you time and money.

3. The Python Community Is Growing

Python has a large number of libraries for scientific computing provided by a huge community. Take a look at PyPi, a repository of software for Python, and explore the full extent of what is being developed in the Python community. NumPy is a great example of this – it’s the core library for scientific computing in Python, introduced in 2006. Recently, NumPy received a $645,000 grant to support its development.

Another good example is SciPy. This library can be used for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, and other tasks popular in science and engineering. SciPy builds on the NumPy array object and is part of the NumPy stack, which includes tools such as Matplotlib, pandas, and SymPy, as well as a growing number of libraries for scientific computing.

4. Python Has a Large Number of Libraries for Data Science/machine Learning

Need more libraries for Python? This popular programming language has a large number of free libraries for data science, machine learning, and data analytics, such as Pandas or Scikit-Learn. Pandas provides fast, flexible, and expressive data structures that make working with “relational” or “labeled” data easy and intuitive. It is one of the most powerful and flexible open source data analysis tools available.

5. There Are Many Deep Learning Frameworks in Python

There are a variety of deep learning frameworks, such as Caffe, TensorFlow, PyTorch, Keras or mxnet. You can choose from many free tools that fit your project and allow you to create deep learning architectures with remarkably few lines of Python code.

6. Python Is a Good Choice for Creating Scraping Software

Python offers a wide range of tools for scraping data. While many languages have libraries that can help with web scraping, Python has the most community support here as well. You can choose between many different scraping ecosystems, such as Scrapy, BeautifulSoup, or Requests. Scrapy, for example, is more than just a library – it’s a great framework designed specifically for scraping the web. It can do a lot of the dirty work for you by providing a structure for your spiders. With Scrapy, you are able to write web spiders in a matter of minutes.

7. Need to Process Tons of Data? Use Python

If you need to process tons of data, you can use PySpark or Hadoop. There’s also an MPI connection for distributed processing if Spark’s overhead is too great for your particular case.

If you use Spark, some engineers recommend developing solutions in Scala, Spark’s “native” language. For many, Python is even a better option because of the full PySpark API.

“(…)Like many developers, I love Python because it’s flexible, robust, easy to learn, and benefits from all my favorite libraries. In my opinion, Python is the perfect language for prototyping in Big Data/Machine Learning,” said Charles Bochet CTO @LuckeyHomes.

8. Code Readability Is One of the Basic Assumptions of Python

“There should be one – and preferably only one – obvious way to do something” is another quote from “The Zen of Python” mentioned at the beginning of this article. As you can see, code readability is one of the most important design principles of Python. Several different programmers can write different programs in Python, but the ideal is that the code is not only similar, but also easy to understand and read. Python code is very readable, some programmers even say that it almost looks like the English language. Why is this important? It is helpful to be able to revise the code months after the product is launched to fix a bug or add a feature. Moreover, this can be easily done by others.

9. Python Too Slow? There Is Cython

Some may object that Python is slower than other programming languages. If you read “Advantages and disadvantages of Python: what are the advantages and disadvantages of the programming language”, you will find that speed is not Python’s strong point. But there is a solution that can increase the speed of the language. It is Cython, a superset of the Python programming language designed to achieve C-like performance with code written primarily in Python. It makes writing C extensions to Python as easy as writing in Python itself. Cython combines the simplicity of Python with the speed of native code. With Cython, speed increases can be achieved from a few percent to several orders of magnitude.

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