* 2 min read

Python is a general-purpose, high-level, interactive programming language. It is light weight, but extremely powerful. It has long been well known for its simplicity. It is relatively easy to learn, read and write. The syntax is straightforward and to the point, allowing you to focus more on the problem at hand rather than on the best way to write the code. Python also has a massive online community that is active in many different domains. Data scientists, whether working with stats, numbers, text or other kinds of data, do not want to be weighed down with overly complicated programming requirements. They just want to get the job done in the easiest and most efficient way possible. This is where Python is the programming language of choice. Python is extremely popular in data science. But why?

Python is easy to learn

Python has an easy to understand syntax, giving it an extremely fast learning curve, as opposed to other related languages such as R. Also, it was designed with simplicity in mind, in order to write code quickly and with as few lines of code as possible.

Python works well with Big data

Python has proven to cope well with massive amounts of structured or unstructured data. It is also faster at processing this data in comparison to R. It can be seen as easily scalable, and can be reliably used to quickly develop large scale applications that are built to work with large datasets.

Wide variety of reliable data science modules and libraries

Since Python has a very active community, there are many available data science libraries that are well-maintained and updated. Libraries and modules are free and bugs are fixed very quickly by the community.

Data visualization is easy

Python makes it very easy to visualize data. Matplotlib is the most popular module for this, and many other modules have been built based on this. You can easily create graphs and charts of all kinds, which can be integrated in to browser based apps.

Machine learning is easy in Python

With Python, machine learning is very easy to implement. Scikit-learn is a popular and easy to use machine learning module. It contains many commonly used supervised and unsupervised classifiers such as SVMs, NBs, etc. You can apply a machine learning classifier on a dataset of your choice within a few minutes.

Python is a jack of all trades

The success of any large scale corporation highly depends on its ability to derive valuable insights from (sometimes unstructured and noisy) data. This involves dealing with (usually BIG) data, data pre-processing and cleansing, running of large-scale algorithms, machine learning, and visualizing/summarizing the insights found in a format that is easy to read by decision-makers. Python, being a general purpose language, has well-known modules for each of these tasks, and a library can be found for any task you need to accomplish.


Mohammad D.

Mohammad D. works with sentiment anlaysis, NLP and Python. He loves to blog about these and other related topics in his free time.

Leave a Reply