Python for Data Science: Understanding the Trend

In this digital era of high technologies, data becomes an essential part of business and enterprise. It is crucial to process and analyze the data as fast as possible with high accuracy.

Since the data volume is huge, managing them becomes a herculean task. Data science engineering helps to overcome data difficulties efficiently. Data science is a field that uses algorithms, scientific methods to extract insights from structured as well as unstructured data which are usually large.

With the Data Science industry booming, today’s Data scientists use many programming languages like Python, R, C++, Java. But the question in mind is ‘Which is the most preferred language by data scientists?’

The answer is undoubtedly Python.

Python is one of the programming languages that help in handling data efficiently. Let’s see why it becomes one of the most used languages.

Python Programming Language: An Overview

Python is an interpreted, object-oriented, high-level programming language. It is simple with an easy-to-learn syntax which reduces the cost of program maintenance. Python supports many modules and packages, which reassures program modularity and code reuse. It has a wide variety of libraries that simplifies the tasks for Data scientists.

Due to its vast features, Python is widely used by companies to analyze data, build web apps and create scalable enterprise applications.

Some of the top companies that use Python are:

  • Google
  • Facebook
  • Instagram
  • Spotify
  • Quora
  • Netflix
  • Dropbox
  • Reddit

What Makes Python the best option for Data science?

Python is used to streamline large complex data sets. Being fast, Python fits well with data analysis. Due to its heavy support and availability of stacks of open-source libraries, Python becomes the number one choice for many data scientists. Let’s see some of its features

Easy to Learn

When compared to other data science languages like R programming, Python promotes a shorter and faster learning curve. Data scientists can easily learn Python as it has a simple syntax and better comprehensibility. Python additionally gives a lot of data mining tools that help inefficient handling of the data. That’s why it’s an ideal tool for data science beginners.


The hyper flexibility of Python makes it highly demandable among data scientists and analysts. Due to this, it is possible to systematize data sets, build data models, create ML-powered algorithms, web services, and apply data mining to manage different tasks in a crisp time.


Comparing with other languages like R, Rust, and Go, Python is much faster and more scalable. Python is used in different industries in multiple fields that can solve a wide range of problems also for the rapid development of applications of all kinds. Hence many companies have migrated to Python.

Choice of data science libraries

Data scientists deal with complex problems by combining computer science, modeling, analytics, statistics, and math skills to bring out the business criteria.

There are Python packages that are specifically tailored for these functions like Pandas, NumPy, and SciPy, Scikit-learn has evolved as a useful and valuable tool for various machine learning and AI tasks.

Python libraries in detail that are widely used in Data Science.


NumPy is a Python library that is used for scientific computations.

It leverages the usage of sophisticated functions and mathematical concepts like linear algebra, and so on.


It is a popular plotting library for designing numerous figures in multiple formats. Matplotlib helps to create scatter plots, bar charts, histograms, etc.


It is the most powerful open-source library of Python. Pandas are very useful in merging, reshaping, aggregating, splitting, and selecting data.


Scikit-learn has evolved as a useful and valuable tool for various machine learning and AI tasks. It is built over SciPy, NumPy, and Matplotlib. It consists of regression analysis, classification models, model selection, etc.

Now, let’s look at how these libraries are utilized in the problem-solving processes. The process includes:

1. Data collection & cleansing

2. Data exploration

3. Data modeling

4. Data visualization & interpretation

Data collection & cleaning

With Python, all sorts of data with different formats such as CSV, TSV, or JSON can be accessed efficiently. Python also supports importing SQL tables directly into the code.PyMySQL is one such library.

Once data extraction is completed, the data cleaning process such as replacing non-values, detecting missing datasets, etc is performed.

Data exploration

Python packages such as NumPy and Pandas helps to identify the property of data, unleash insights from the data, and helps to process and manipulate easily and efficiently.

Data modeling

Exploring the basics of machine learning becomes easy and effective with Python. It has a variety of packages supporting various math functions such as Numpy, CVXOPT, SymPy, SciPy, Scikit-learn helps to apply machine learning algorithms to data without any complexities.

Data visualization & interpretation

Visual information is much easier to operate and understand. There are diverse visualization packages available and that makes Python the most used tool not only for data analysis but for data science too. Matplotlib and Plotly are few packages which is a perfect solution for data science projects involving advanced graphs and visuals.

Python in Data science has empowered many data scientists to accomplish more in less time. Python is more effective, easy to understand, highly adaptable, and can work in any environment. Additionally, it can be integrated with other programming languages. Hence Python has emerged as the most preferred language among data analysts and data scientists.

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