What Is Python?

Python is a programming language taking over different aspects of the technology sector. Its most notable contributions are in artificial intelligence and big data analytics. These two disciplines are considered the most important field of work for the future, and Python is becoming an important part of this field.


Python has risen through the ranks and has become the third most common programming language in recent times. Among its many uses, it is particularly useful for statistical and academic models, which allow it to be a suitable candidate for working with different forms of data.


The uses of Python are extensive. These include web development, simulations, creating automated reports, and other finance or business-related practices. Furthermore, Python has also successfully overtook R in terms of popularity among data scientists.


This has been made possible thanks to Python being a general-purpose language. Consequently, using a simpler syntax, it becomes easier to collaborate with different parties and organizations.

What is Data Science?

Data is a key part of proceedings for any form of business organizational activity. Primary or secondary data allows an organization to understand how things have gone in the past, how they are going now, and how they should go in the future. We can use data to understand how a certain outcome reached that point and how things could have gone if certain variables were changed.


Using data, we can make forecasts for the future and plan accordingly to get the best possible results. However, data cannot just be left on its own to be sorted and made sense of. It needs to be organized and managed to be in a readable format for the people who need to use it. This is where data science comes in.


By definition, data science is the field of studying data, making sense of it, and organizing it in a way that benefits those using it. The concepts that data scientists use can be employed in statistics, mathematics, computer science, and data mining.

The Relationship between the Two

Python is often considered the most important programming language for data science. Unlike other programming languages that need to be learned, Python can be understood with relative ease even if you have no prior experience. Furthermore, Python is an open-source language that is free to use and flexible to understand.

Importance of Python for Data Science

There are several reasons why data scientists use Python to write programs required to organize and make sense of data.

The Benefits of Python:

Simple Syntax – Easy to Understand

Many people will never learn how to code because the prospect of writing lines of complex code frightens them. Python is a simple programming language to learn, making it the ideal language for people hoping to get into programming. This is possible thanks to Python’s ability to use fewer lines to get a task done compared to other programming languages that are out there.


Python is preferred because it gives you the chance to ‘play’ with the language to get a better grip on things. You aren’t writing enormous amounts of code like you would with other programming languages.


By using Python, you can do a lot more with the tools you have. Python is an ideal language if you want to develop websites and applications with various features and functions. It is ideal for when you want to get more done with a language that requires minimal effort.

Open Source

Who doesn’t love a free resource? Python is a community-based coding language available for free. Being open source, it can be run on different platforms, and there are multiple libraries to support it and add to its functionality, such as data visualization, data manipulation, machine learning, natural language processing, and mathematics.

Using Python in Data Science

With data science, there are five stages involved. These are data gathering, refinement, exploration, modeling, and visualization.

Data Gathering

There are millions of data sources, and having the right data is imperative. Using Python, you can sift through different data functions and data libraries. Among the best is NumPy or Numerical Python. Using NumPy, you can identify which dataset should be used with which model. By doing this, you will be able to save yourself a lot of time trying to find the right data to use. With Python, the time-consuming task of going through troves of data becomes a lot easier.

Data Refinement

Once you have the data, you need to make sense of it. Data refinement is when you take the unstructured data you received and structure it to be easily understandable. As a data scientist, you are tasked with cleaning data to make it ready for processing. With Python, you use the language to refine unstructured data and input the correct values needed for processing. This is a difficult process and having the right data fed to you is important because if the data is incorrect, the models will cease and give an error.

Data Exploration

After the data has been refined and all excess, unnecessary information is removed, you have a set of data ready to be used. Python will allow you to identify different patterns in your data and extract all important information from it.

Data Modeling

There are tables with different data sets or entities used to make different predictions in a database. These models are built using a python-based algorithm that will show you how different functions are to be executed. Furthermore, each python model has different variables or constants, which are used to deliver results and reach certain conclusions.

Data Visualization

As the name suggests, this is a process where data is given a readable form. Many different data visualization libraries use Python, which includes: Matplotlib, pygal, Plotly, and Seaborn. Putting data into a readable form will help all concerned parties better understand what is being said or explained. This stage is important because data is eventually used further, and if it isn’t in a readable form, it is useless.

Final Thoughts

Python is an extremely important part of any data scientist’s tool kit. It makes the job of extracting, cleansing, managing, and structuring data simpler for everyone involved, and it also allows for large volumes of data to be dealt with at the same time. The simple, open-source nature of Python makes it an invaluable tool for data scientists, providing them with an easy-to-use language that makes developing algorithms less complicated and time-consuming.