Why Your Company Needs Python for Business Analytics

By Hussein on 17 Aug 2020

Not just another language

Python is a multi-paradigm programming language: a sort of Swiss Army knife for the coding world. It supports object-oriented programming, structured programming, and functional programming patterns, among others. There’s a joke in the Python community that “Python is generally the second-best language for everything.”

Python is known for its simplicity and readability that allows developers to write much less code compared to other programming languages. Here is a task the required to read a file on the system in Python you would write the following code:


with open("test.txt", 'r', encoding = 'utf-8') as f:

While to perform the same task but in Java, you would have to write the following code:


Most wanted language

Stack Overflow 2020 survey showed that Python is one most used programming languages. It came behind Javascript, HTML/CSS, and SQL.

Python just came after Rust and TS in the most loved programming language survey and the first in most wanted language.


For these reasons, Python attracts lots of developers and also has a very large developer community which in turn brings out large support to everyone. Also, Python has a very large library, which eases lots of tasks.


                public class ReadFromFile2
                public static void main(String[] args)throws Exception
                File file = new File("C:UsersdanielDesktoptest.txt");
                BufferedReader br = new BufferedReader(new FileReader(file));
                String st;
                while ((st = br.readLine()) != null)

A huge ecosystem

A typical data analysis task involves the following steps:

  • Read dataset from a data source.
  • Apply filters, aggregations, and other requirements.
  • Create easy to understand visualization charts and graphs.
  • In some cases, machine learning algorithms could be utilized for more advanced analytics.

With Python’s ecosystem eases these tasks for the developers. For reading data from the source and applying filters Pandas is your choice. If the task involves statists and heavy numerical computations then use Numpy and Scikit-learn. For visualization Matplotlib and Seaborn are the standards. 

In a lot of cases the user wants to get data from a website instead of a data source, in this case,  Beautifulsoup and Scrapy help extract data from the internet.

Finally, machine learning is an incredibly high computational technique that involves heavy mathematics like calculus, probability, and matrix operations over thousands of rows and columns. All this becomes very simple and efficient with the help of Scikit-learn, Tensorflow, PyTorch, and a lot of other machine learning libraries in Python.



In this blog, I tried to demonstrate Python abilities and most popular libraries, however, there are many tools and third-party libraries available in Python’s ecosystem. Python allows your company to get data, analyzed it, and create visualizations quickly without the need for a large development team, this allows fast prototyping for different kinds of ideas. Find out how Senna Labs could help your company with its analytics projects by leveraging the Python ecosystem.

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