Why is Python popular for data science?
Python is a popular high-level programming language used primarily for data science, automation, web development, and artificial intelligence. It is a general-purpose programming language supporting functional programming, object-oriented programming, and procedural programming. Over the years, Python has been known to be the best programming language for data science, and it is commonly used by major tech companies for data science tasks.
In this tutorial, you will learn why Python is so popular for data science and why it will remain popular in the future.
What can Python be used for?
As said earlier, Python is a general-purpose programming language, which means it can be used for almost anything.
A common application of Python in web development is using Django or Flask as a backend for a website. For example, the Instagram backend runs on Django, and it’s one of the biggest deployments of Django.
You can also use Python for game development with Pygame, Kivy, Arcade, etc. although it is rarely used. Mobile app development is no slouch, Python offers many app development libraries such as Kivy and KivyMD that you can use to develop cross-platform apps; and many other libraries like Tkinter, PyQt, etc.
What is Data Science?
According to Oracle, data science combines several fields, including statistics, scientific methods, artificial intelligence (AI), and data analytics, to extract value from data. This includes preparing data for analysis, including cleaning, aggregating, and manipulating data to perform advanced data analysis.
Data science is applicable in different industries, and it helps to solve problems and learn more about the universe. In the healthcare industry, data science helps doctors use past data to make decisions, such as a diagnosis or the right treatment for a disease. The education sector is no slouch, now you can predict school dropouts, all thanks to data science.
Python has a simple syntax
What else can make programming so much easier than having an intuitive syntax? In Python, you only need one line to run your first program: just type print(“Hello world!”) and run – it’s as simple as that.
Python has a very simple syntax and makes programming much easier and faster. There’s no need for braces when writing functions, no semicolons are your enemy, and you don’t even need to import libraries before writing basic code.
This is one of the advantages of Python over other programming languages. You are less likely to make mistakes and you can easily notice bugs.
Data science is a complex field that you cannot tackle without needing help. Python offers all the help you need thanks to its huge community. Whenever you get stuck, go through it and your answer is waiting for you. Stack Overflow is a very popular website where questions and answers are posted on programming issues.
If your problem is new, which is rare, you can ask questions and people would be willing to provide answers.
Python offers all the libraries
You badly need water and you only have two cups on the table. One is a quarter full of water while the other is nearly full. Would you wear the cup with a lot of water or the other, although they both have water? You would want to wear the cup with a lot of water because you really need water. This is Python related, it offers all the libraries you would need for data science, you definitely wouldn’t want to use another programming language with only a few libraries available.
You will have great experience working with these libraries as they are really easy to use. If you need to install a library, search for the library name on PyPI.org and follow the instructions near the end of this article to install the library.
Numerical Python – NumPy
NumPy is one of the most widely used data science libraries. It allows you to work with numerical and scientific tasks in Python. Data is represented using arrays or what you can call lists, which can be in any dimension: 1-dimensional array (1D), 2-dimensional array (2D), 3-dimensional array (3D), etc
Pandas is also a popular data science library used in data preparation, data processing, and data visualization. With Pandas, you can import data in different formats such as CSV (comma separated values) or TSV (tab separated values). Pandas works like Matplotlib because it lets you create different types of plots. Another cool feature offered by Pandas is that it lets you read SQL queries. So if you’re connected to your database and want to write and run SQL queries in Python, Pandas is a great choice.
Matplotlib and Seaborn
Matplotlib is another awesome library offered by Python. It was developed on MatLab – a programming language used primarily for scientific and visualization purposes. Matplotlib allows you to plot different types of graphs with just a few lines of code.
You can plot graphs to visualize all the data, help you better understand your data, or give you a better representation of the data. Other libraries like Pandas, Seaborn and OpenCV also use Matplotlib to plot sophisticated graphs.
Seaborn (not Seaborne) is like Matplotlib, just that you have more options – to give different colors or tints to different parts of your graphs. You can draw nice graphs and customize the appearance to improve data representation.
Open computer vision – OpenCV
Maybe you want to build an optical character recognition (OCR) system, document scanner, image filter, motion sensor, security system, or anything else related to computer vision, you should try OpenCV. This amazing and free library offered by Python allows you to create computer vision systems with just a few lines of code. You can work with images, videos, or even stream and roll out your webcam.
Scikit-learn – Sklearn
Scikit-learn is the most popular library used specifically for data science machine learning tasks. Sklearn offers all the utilities you need to mine your data and create machine learning models in just a few lines of code.
There are various machine learning tasks such as linear regression (single and multiple), logistic regression, k nearest neighbors, naive arrays, support vector regression, random forest regression, polynomial regression , including classification and grouping tasks.
Although Python is simple due to its syntax; there are tools specifically designed for data science. Jupyter notebook is the first tool, it is a development environment built by Anaconda, to write Python code for data science tasks. You can instantly write and run codes in cells, bundle them, or even include documentation, as its markdown capability allows.
A popular alternative is Google Colaboratory, also known as Google Colab. They are similar and used for the same purpose, but Google Colab has more advantages due to its cloud support. You have access to more space, without having to worry about your computer’s memory running out. You can also share your notebooks, sign in and access them on any device, or even save your notebook to GitHub.
How to Install Any Data Science Library in Python
Since Python is already installed on your computer, this step-by-step section will guide you through installing any data science library on your Windows computer. NumPy will be installed in this case, follow the steps below:
- hurry To start up and type ordered. Right click on the result and choose Execute as administrator.
- You need PIP to install Python libraries from PyPi. If you have already done this, feel free to skip this step; if not, please read how to install PIP on your computer.
- Type pip install numpy and press Enter to run. This process will install NumPy on your computer and now you can import and use NumPy on your computer. This process should look like the screenshot below, ignore the warning and blanks. (If you’re using Linux or macOS, just open a terminal and enter the install pip order).
It’s time to use Python for data science
Among other programming languages like R, C++ and Java; Python is best for data science. This tutorial has told you why Python is so popular for data science. Now you know what Python offers and why big companies like Google, Meta, NASA, Tesla, etc. use Python.
Did this tutorial convince you that Python will remain the best programming language for data science? If yes, go ahead and build beautiful data science projects; help make life easier.
For advanced data analysis, Python is better than Excel. Here’s how to import your Excel data into a Python script using Pandas!
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