Python is a popular high-level programming language primarily used in data science, automation, web development, and artificial intelligence. It is a general-purpose programming language that supports functional programming, object-oriented programming, and procedural programming. Over the years, Python has been known as the best programming language for data science, and is commonly used by big tech companies for data science tasks.
In this tutorial, you’ll learn why Python is so popular for data science and why it will still be popular in the future.
What can Python be used for?
As mentioned earlier, Python is a general purpose programming language, which means that it can be used for just about everything.
One of the popular applications of Python in web development is to use Django or Flask as a backend for a website. For example, the Instagram backend runs on Django, which is one of the largest Django deployments.
You can also use Python to develop games with Pygame, Kivy, Arcade, etc.; Although it is rarely used. Mobile app development is not left out, as Python offers several application development libraries such as Kivy and KivyMD which you can use to develop multi-platform applications; And many other libraries like Tkinter, PyQt, etc.
The main talk of this tutorial is the application of Python in data science. Python has been proven to be the best programming language for data science and you will know why in this tutorial.
What is data science?
According to Oracle, data science combines several areas, including statistics, scientific methods, artificial intelligence (AI), and data analysis, to extract value from data. It includes preparing data for analysis, including data purification, aggregation, and processing for advanced data analysis.
Data science is applicable in different industries, helping to solve problems and discover more about the universe. In the health industry, data science helps clinicians take advantage of past data in making decisions, for example, the diagnosis, or appropriate treatment of a disease. The education sector has not been left out, you can now expect students to drop out of school, all thanks to data science.
simple python language
What could make programming so much easier than having an intuitive syntax? In Python, you only need one line to run your first program: simply type print(“Hello World!”) And run – it’s that easy.
Python has a very simple syntax, and it makes programming a lot easier and faster. There is no need for curly braces when writing functions, no semicolon is your enemy, and you don’t even need to import libraries before writing the underlying code.
This is one of the advantages that Python has over other programming languages. You have less inclination to make mistakes, and you can easily notice mistakes.
Data science is a complex field that you cannot do without needing any help. Python offers all the help you need through its vast community. Whenever you get stuck in a problem, just browse through it and you will find your answer waiting for you. Stack Overflow is a very popular website where questions and answers to programming problems are posted.
If your problem is new, which is rare, you can ask questions and people will be willing to provide answers.
Python offers all libraries
You desperately need water, and you only have two cups on the table. One is a quarter full of water and the other is nearly full. Will you carry the cup with a lot of water or the other cup, even though they both contain water? You may want to carry the cup with a lot of water because you really need the water. This is related to Python, it provides all the libraries you will ever need for data science and you definitely won’t want to use another programming language with so few libraries available.
You will have a great experience working with these libraries because they are really easy to use. If you need to install any library, search for the library name in PyPI.org and follow the instructions at 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 matrices or what you might refer to as lists, which can be in any dimension: a one-dimensional (1D), two-dimensional (2D), three-dimensional array, and so on.
Pandas is also a popular data science library used for data preparation, data processing, and data visualization. With Pandas, you can import data in different formats like CSV (Comma Separated Values) or TSV (Tab Separated Values). Pandas works like Matplotlib because it allows you to make different types of plots. Another great feature offered by Pandas is that it allows you to read SQL queries. So, if you’ve connected to your own database, and you want to write and run SQL queries in Python, Pandas is a great option.
Matplotlib and Seaborn
Matplotlib is another great library of Python offerings. It was developed on top of MatLab – a programming language used primarily for scientific and visualization purposes. Matplotlib allows you to draw different types of graphs with just a few lines of code.
You can plot graphs to visualize any data, which can help you gain insights from your data, or give you a better representation of the data. Other libraries such as Pandas, Seaborn, and OpenCV also use Matplotlib to plot complex graphs.
Seaborn (not Seaborne) is just like Matplotlib, only you have more options – to give different parts of your graphs different colors or gradients. You can draw beautiful graphs and customize the appearance to make the data representation better.
Open Computer Vision – OpenCV
Maybe you want to build an Optical Character Recognition (OCR) system, document scanner, photo filter, motion sensor, security system or something else related to computer vision, you should try OpenCV. This great and free Python library allows you to create computer vision systems with just a few lines of code. You can work with photos, videos, or even a webcam feed and publish it.
Scikit-Learn is the most popular library used specifically for machine learning tasks in data science. Sklearn offers all the utilities you need to take advantage of your data and build machine learning models in just a few lines of code.
There are many machine learning tasks such as linear regression (simple and multiple), logistic regression, k-nearest neighbors, naive cells, vector regression support, random forest regression, and polynomial regression, including classification and clustering tasks.
Although Python is simple due to its syntax; There are tools that are specifically designed with data science in mind. Jupyter notebook is the first tool, a development environment created by Anaconda, to write Python code for data science tasks. You can write codes and immediately run them in cells, compile them, or even embed documentation, as provided by their markdown ability.
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 can access more space, and you don’t have to worry about your computer storage getting full. You can also share your notebooks, log in and access any device, or even save your notebook to GitHub.
How to install any data science library in Python
Since you already have Python installed on your computer, this step-by-step section will guide you on how to install any data science library on your Windows PC. NumPy will be installed in this case, follow these steps:
- Journalism Begins And type poultice. Right click on the result and choose Run as administrator.
- You need PIP to install the Python libraries from PyPi. If you already have it, feel free to skip this step; If not, please read how to install PIP on your computer.
- Type numpy installation point and press Enters escape from. This process will install NumPy on your computer and you can now import and use NumPy on your computer. This process should resemble the screenshot shown below, ignoring the warning and blank spaces. (If you are using Linux or macOS, just open Terminal and enter a file install point ordering).
It’s time to use Python for data science
Among other programming languages such as R, C++, and Java; Python is considered the best in data science. This tutorial walks you through why Python is so popular for data science. Now you know what Python has to offer and why major companies like Google, Meta, NASA, Tesla, and others use Python.
Did this tutorial convince you that Python will still be the best programming language for data science? If yes, then start building good 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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