Python is one of the most prominent programming languages that is used by developers around the world. Python is not only a favorite language for developers but it is also a prime language for data scientists. Data scientists prefer this high-level programming language due to its logical paradigms. However, there are many more reasons behind its popularity.
The aim of this article is to examine the relevance of this programming language to data science in great detail.
Overview of python
The inception of python dates back to 1980. It was during this time that Guido van Rossum came up with the idea to draft a programming language that would be the follow up of the ABC programming language. Over a period of time, this language saw many revisions and developments. It was ultimately in the year 2008 that python 3.0 came into existence. When this version of the programming language was revealed, hardly did we know that it would grow up to be one of the most significant contributors to machine learning in the times to come. With various iterations and updates, python developed further and overcame the various flaws that were there in the original version.
Python and data science
Python is one of the most preferred languages for data science. This is not only due to the simplicity that python provides but also due to the range of benefits associated with this programming language. It needs to be noted at this point in time that data scientists often come from various backgrounds. Various statisticians, mathematicians, and technicians choose data science as their main career option. As such, they need a programming language that is not only simple but also easy to understand. In addition to this, the coding experience that is provided by python is very immersive and engaging. Moreover, there is a range of online resources that can help us to get started with Python from the comfort of our home. The syntax of Python is easy to understand and write. This has led to its popularity among amateur programmers. Python is also an open-source language. It is easily accessible and available to the general public. This means that the programming language is devoid of any upfront costs. As per the 2020 Stack Overflow Survey, Python is one of the most popular programming languages among both developers and data scientists. According to this survey, the total number of developers and machine learning engineers that use python for data science is about 7 million. The popularity of python has been boosted by tutorials, seminars, and conferences that are establishing a unique connection between the data science community around the world. The most popular conference in this regard is PyCon which is the knowledge development platform for this programming language.
Python Libraries
There are a large number of libraries that are associated with Python. The primary goal of these libraries is to help in data visualization and analysis. They also help in making the process of data processing, data cleaning, and other machine learning tasks simpler. The most important library that is associated with Python is NumPy. This library is mostly used for executing different types of mathematical tasks and working with matrices. The second important type of library in Python is Pandas. This library facilitates the use of tabular data for effective analysis of various types of structured data sets. Another library associated with Python is called seaborn. This type of library is effective for the process of data visualization. It allows the use of various statistical graphs and other types of scatter plots and histograms so that the information become understandable. One more important library associated with Python is called Requests. Whenever we need to scrape data from a website, this library comes to our aid. It helps in configuring different types of HTTP requests.
Other libraries
There are other types of machine learning libraries used in Python. The most popular machine learning library is called sci-kit learn. This library is very important when it comes to the execution of different supervised and unsupervised learning tasks. There are other machine learning algorithms that are associated with this library. These include random forest model, gradient boosting, and support vector machine. Tensorflow is another important library that helps in building various types of deep neural networks. It is one of the most preferred libraries for scientists who are dealing with deep learning. This type of library is very suited for professionals with a lot of experience in the field.
