Python for Data Science: What Makes It Perfect?

Everyone is talking about Python's capability as a programming language for data science. Besides web development, Python is taking over big data analytics and the Artificial Intelligence industry. Python programming language is now surpassing R as the topmost choice for data science applications.

There are various reasons for Python to be one of the best data science languages. It is the third most popular programming language, according to TIOBE's index. Python's usefulness in academic and statistical models makes it a good contender for working with data.

Python has scientific applications in multiple industries. Even those who have minimum experience in engineering can work with Python. The plethora of deep learning frameworks and Python APIs equip developers with the right tools to build technically advanced data science applications.

This article will focus on the reasons that make Python the right choice for data science. We will also look at how Python went beyond R and enabled developers to build modern-day data-based solutions.

Why does Python for Data Science Make Sense?

Python is much more robust and dynamic than other programming languages for data science. It offers many advantages in terms of readability, accessibility, and scalability for both customer-centric and enterprise-based data science applications.

Here are the reasons why data scientists prefer Python programming language for Complex data-based application:-

1. Simplicity

Python is a simple programming language with clear syntax. It allows developers to write applications in fewer lines of code as compared to other programming languages. Simplicity makes it a perfect solution for beginner data scientists as well. Because of this, it takes less time to code, allowing data science startups to reach conclusions quickly and build better products.

2. Data science libraries

Python developers know that to work with data science, libraries are essential. There are many different frameworks and libraries for multiple purposes in data analytics. There are various libraries for data handling, numerical computing, and scientific calculations. Some of these include Pandas, NumPy, SciPy, among many others. Libraries are crucial because they provide enhanced functionality to the application.

3. Deep learning frameworks

Deep learning is an area that deals with a lot of data. Python programming language has several deep learning frameworks for building intelligent applications. There are frameworks like TensorFlow, Keras, PyTorch, and many others with inbuilt capabilities for creating the deep learning architecture. However, it still uses fewer lines of code for writing the application.

4. Wide community

Python for data science has one of the biggest communities in the programming circles. It is growing at a rapid rate. NumPy and SciPy are great examples, where the library founders raised over $600,000 in grants to improve its effectiveness. Community members provide solutions to complex algorithmic and scientific computing problems. There is a lot of material and documentation on using python in data science.

5. Data processing support

There are various ways through which you can process tons of data in Python. There's PySpark and Hadoop, both of which enable Python developers to process data with ease. Python is a powerful programming language that provides enhanced functionalities with easily readable code. It is flexible and allows scaling of applications when there is a high amount of data.

How does Python help in data science?

There are four major steps to data science. All of them involve complex problem solving, which requires a powerful programming language. Data science using Python helps in solving all the issues.

Data science includes data gathering and refinement, data exploration, data modeling, and data visualization. Python has the right data science tools for each stage. There are libraries that can help data scientists visualize complex problems and build algorithmic solutions through Python.

Here's how Python helps in data science –

Stage 1

The first stage is data gathering. When you need to work with data, the right kind of data is extremely important. However, if there are millions of data values, how can you identify which one you should work with?

Python allows developers to work with such data through functions and libraries. NumPy is one of the best data science libraries in Python that can help identify which datasets you need to use for the model. Data gathering is a time-consuming task, which is simplified using Python.

Stage 2

The next stage is data refinement or simply data cleaning. There's a lot of dirty and unstructured data that can't be processed in its original form. Therefore, developers need to clean that data so that it becomes usable for processing.

Data cleaning involves using Python for data science to refine the data and understand input values useful for the process. Data cleaning is a challenging task, but without the right data, the model will not work.

Stage 3

Data exploration is the most significant step. It involves developing a deeper understanding of the data. Data science using Python helps in identifying the patterns, insights, and useful information from the data. Exploration is about the discovery of concepts and data that would provide the most value.

Stage 4

Data modeling is based on building the relationship between different data values in Python. Every object is a separate entity. The tables in the database include these entities through which potential predictions are made.

It involves Python-based algorithms for building models that showcase how the function will be executed. Each Python model involves variables and constants that are used to further reach conclusions and deliver results.

Stage 5

There are hundreds of data visualization libraries in Python. Matplotlib, pygal, Plotly, Seaborn are some of the popular data science libraries in Python for visualizing the data. Placing the data in a visual context helps to understand what we are trying to achieve with it.

It is a valuable stage to identify the trends and patterns that are not exposed in the data at first sight. It provides a complete overview of what the model looks like and how it works.

Conclusion: Python for Data Science is Booming 

Is Python the best language for data science? It is increasingly becoming a popular choice. There are multiple aspects to data science using Python. However, the power and capabilities of Python ensure that developers never face any problems. Complex algorithmic and AI-based problems are now solved using Python in the best manner.

If you are looking for Python-based data science applications, BoTree Technologies is a leading Python development company that can help. Contact us today and talk to our experienced Python developers.