What Makes Python such an Effective tool for Data Science
You could easily say that Python is a perfect mix of all good things in a number of different languages. It is something between an object oriented language and scripting language. It is strangely well suited for a multitude of different purposes. The rather easy learning curve makes it more attractive to the coders. We will focus on suitability of Python for data science.
A lot could be said about how Python is a great tool in general and I am willing to talk, though sparingly, about that later in the article. The libraries really deserve to be in the front row. Let us start with a little history. The idea of using Python for numeric analysis was pursued by the Python community back in the 1990s. ‘Numeric’, an extension of Python which would work in the lines of Matlab to support numeric analysis was developed. What we know as NumPy today was the evolved version of Numeric.
Matplotlib was developed by the plotting functionalities from Matlab. What we know today as SciPy is a bundle of libraries dedicated to scientific computing which were built around NumPy and Matplotlib. As for machine learning, Python has the Scikit-learn library providing a common interface for machine learning algorithms. The pandas library helps in data manipulation much like R.
The Python libraries really make a huge difference when it comes to getting something done rapidly. The extensive libraries come with great IOT opportunities.
The other advantages of Python
What strikes as the most important advantage of Python as a programming language is the ease of learning. Python has an intuitive syntax. It internally handles a lot of complexities so it comes across as a rather simple language to the users.
It is easy to write something quickly on Python. It comes with great scripting potential. Data science programming needs to be agile and Python is just what the doctor prescribes.
The robust Python community never lets it lag behind. You can count on Python to be up to date at all points. They Python community is ever so active in terms of adopting changes and maintaining security.
It is a good starting point if you have never coded before. It is also a good starting point for your career in data science. Using Python data science is undeniably a win win situation. If you are going to use Python, you should be aware of some of its shortcomings. Python has some limitations in terms of speed. It is also weak in mobile computing and has some design restrictions. Despite all these, it is still one of the best in the business.