What is Python?

1)- Python is an interpreted programming language

In layman's terms, when we say that Python is an interpreted language, it means that code is not directly converted into machine code (binary code) that a computer can understand and execute. Instead, Python programs are run by an interpreter, which reads and executes the code line by line. Imagine you have a set of instructions written in a foreign language. In the case of compiled languages like C++, these instructions would need to be translated into the native language of the computer before they can be executed. This translation process is like translating an entire book from one language to another and then reading it. Once the translation is done, the computer can execute the program directly. On the other hand, an interpreted language like Python works more like having an interpreter present with you who can understand and execute the instructions on the spot without prior translation. It's as if you have someone who can read and understand the foreign language directly as you speak it. So, when you write Python code, you don't need to compile it before running it. The Python interpreter reads each line of your code, converts it into machine instructions, and executes them immediately. This immediate interpretation allows for a faster development process, easier debugging, and a more interactive programming experience.

2)- Python is a dynamic programming language

Let’s think about a toolbox as a collection of various tools.. In some cases, when you want to use a particular tool, you need to know exactly what it looks like, its specific purpose, and how to use it. You can only use a tool if you have prior knowledge of its exact specifications.
Now, imagine a toolbox where the tools are more flexible and adaptable. In this toolbox, you don't need to know the specific details of each tool in advance. You can pick up a tool and easily change its shape, function, or even create new tools by combining existing ones.
In the same way, Python is like that flexible toolbox for programmers. It allows them to work with code in a more versatile and adaptable manner. They can create and change variables, which are like containers for storing information, without worrying about specifying their exact type or structure beforehand.
For example, in some programming languages, you need to say, "This variable will always store a number" or "This variable will always store text." But in Python, you don't have to make that decision in advance. You can assign a number to a variable and later change it to hold a word or a list of items.
Python's flexibility also extends to other aspects. It allows programmers to modify their code while it's running, easily add new features, and dynamically use different tools (libraries) as needed.
So, when we say Python instead of other programming languages?

Faster Development Process

Here development means you are writing a bug free code fast. Imagine you're writing a story using two different types of pens: a regular pen and a special pen that changes color when you make a mistake. The regular pen represents other programming languages, while the color-changing pen represents Python. When you're writing with the regular pen, it doesn't notify you when you make a mistake. You have to go back and proofread the entire story carefully, word by word, to find and correct errors. It can be time-consuming and challenging to spot mistakes, especially when they are small or hidden.
However, when you use the color-changing pen (Python), it instantly shows you where mistakes are. As soon as you make an error, the ink changes color, making it easy to identify and correct the problem without having to search the entire story. This saves time and makes the correction process more efficient.
Similarly, in Python, the moment you run your code, it examines it line by line and instantly detects errors. It provides specific error messages that highlight where the issue occurred, saving you from having to meticulously search through all the code. These error messages are often descriptive and give hints about what went wrong, making it easier for programmers to understand and fix the error.

Easy language compared to other programming languages.

In python syntax to write code is very easy.Example Let’s write a code to store a list of numbers.

In Java: List Numbers=Arrays.asList(3,4,2,3,4,5,6,7);

In C++: int Numbers[8] = {3,4,2,3,4,5,6,7};

In python : Numbers=[3,4,2,3,4,5,6,7]

So we can compare syntax of python with other languages.That’s why python is the only language that is used in variety of fields(like Biology,Mechanical,Electrical,Commerce,Robots etc.) and can be understood by a non-IT person or a beginner.

Large variety of libraries and community worldwide

In programming, a library is a set of pre-written code that provides specific functionalities or solves common problems. It's like having a toolbox filled with useful tools for different tasks. These tools are created by other programmers and shared with the community, so you don't have to start from scratch when you face a specific programming challenge.

For example, if you need to perform complex mathematical calculations in your program, there might be a math library available. This library contains functions and formulas that can help you with those calculations, saving you time and effort. You can simply use the functions from the library instead of writing all the code yourself. Similarly, if you're building a website, there might be a web development library available. This library could have pre-built components, templates, and functions that help you create interactive web pages more easily. Python's large community and extensive library ecosystem exist because Python is a language that people find enjoyable to work with. The friendly and supportive community, along with the wide range of available libraries, makes Python a vibrant and rich programming environment where programmers can easily find help, collaborate, and leverage existing resources to build their projects.

Disadvantages of Python

A Slow Programming language

How strange it is to hear that python is very slow language compared to other languages.As we discuss Python is an interpreted language, which means that code is executed line by line, translating each line into machine instructions at runtime. This interpretation process adds an overhead compared to languages that are compiled directly into machine code before execution. In many cases if we try to run a program on same system in Java,C and Python,Python performance can be 10-20 times slower than rest of two.

WTF! If it is so slow why it is used worldwide inspite of being easy.

See, Speed of a language is in terms of milliseconds.Suppose a program written in java run in 1-2 milliseconds than python may takes 15-20 milliseconds.Now takes “Instagram” into account.Instagram backend is written in python.When we click a button on Instagram, a new page is displayed(which is done by python) in 20 milliseconds .What if backend was made in Java,that page can be rendered in 2-3 milliseconds.

Now tell is it matters a page rendered in 2 milliseconds or 20 milliseconds? Obviously No!.Because our eyes cannot see this small difference of speed in milliseconds.That’s it speed does not matter in many cases.But If we have to build a game like “Pubg” where in one moment there can be lot of stuff happening like counting bullets from a gun,send voice of one player to other 4 players,displaying game graphics in full HD etc.Here if game lags even in 1 millisecond there will a crash.

That’s why python is not used in gaming at advance level because it is slow.

Application of Python in IT

Web Development: Python offers frameworks like Django and Flask, which are popular for building dynamic and robust web applications. It provides tools for handling databases, managing server-side logic, and creating interactive web interfaces.

Automation and Scripting: Python's ease of use and readability make it an excellent choice for automating repetitive tasks and scripting. It is commonly used for writing scripts to automate file operations, perform system administration tasks, and create batch processes.

Internet of Things (IoT): Python is suitable for IoT applications due to its simplicity and compatibility with a wide range of devices. It can be used to control and communicate with sensors, collect and process data, and build IoT applications and prototypes.

Applications of Python in NON-IT Sectors:

Data Analysis and Visualization: Python's data manipulation libraries such as Pandas and data visualization libraries like Matplotlib and Seaborn make it useful for analyzing and visualizing data in fields like business, finance, market research, and social sciences.
Scientific Research: Python is widely used in scientific research across disciplines such as physics, chemistry, biology, and astronomy. It provides tools for data analysis, simulations, and visualization, enabling scientists to analyze and interpret research data effectively.
Finance and Economics: Python is popular in the finance industry for tasks such as quantitative analysis, risk management, algorithmic trading, and financial modeling. Libraries like NumPy, Pandas, and scikit-learn are extensively used for these purposes.
Education: Python is increasingly used as an introductory programming language in educational institutions due to its readability and beginner-friendly syntax. It helps students grasp fundamental programming concepts and logic before moving on to more complex languages.
Automation and Productivity: Python's simplicity and ease of use make it an excellent choice for automating tasks and increasing productivity. It can be used to create scripts that automate repetitive processes, handle data processing, generate reports, or streamline workflows.
Geospatial Analysis: Python libraries like GeoPandas and PySAL are used for geospatial analysis, allowing professionals in fields such as geography, urban planning, and environmental science to analyze and manipulate geographic data.
Art and Design: Python can be used in creative fields for generative art, visualizations, and interactive installations. Libraries like Pygame, Processing, and OpenCV enable artists and designers to create dynamic and interactive visual experiences.
Healthcare and Medical Research: Python is used in healthcare and medical research for tasks such as data analysis, image processing, and machine learning in medical imaging, genomics, and bioinformatics.

Python and Data Science

Python is widely used in data science for several reasons:
Extensive Libraries: Python has a rich ecosystem of libraries and frameworks specifically designed for data science. Libraries such as NumPy, Pandas, and scikit-learn provide powerful tools for data manipulation, analysis, and machine learning. These libraries simplify complex operations, making it easier to work with large datasets and perform advanced statistical analysis.
Data Visualization: Python offers libraries like Matplotlib, Seaborn, and Plotly, which provide a wide range of data visualization capabilities. These libraries enable data scientists to create meaningful and interactive visualizations to explore and communicate insights effectively.
Machine Learning and AI: Python's libraries, such as TensorFlow, Keras, and PyTorch, are extensively used for developing and training machine learning models. Python's simplicity and readability make it easier to prototype, experiment, and deploy machine learning algorithms. It provides a flexible and accessible environment for implementing complex AI techniques.
Easy to Learn and Use: Python has a clean and straightforward syntax, making it an ideal language for beginners in data science. Its simplicity and readability lower the barrier to entry, allowing data scientists to focus on solving problems rather than struggling with complex syntax.
Strong Community Support: Python has a large and active community of data scientists and developers. The community shares resources, best practices, and open-source projects, making it easier to find support and collaborate on data science projects. This collaborative environment contributes to the growth of the Python data science ecosystem.
Integration and Compatibility: Python seamlessly integrates with other languages such as R and SQL, allowing data scientists to leverage specialized tools and libraries for specific tasks. Python also interfaces with popular databases and big data frameworks, making it suitable for working with diverse data sources.
Rapid Prototyping and Development: Python's flexibility and quick development cycles make it well-suited for rapid prototyping and experimentation. Data scientists can iterate and test different models and algorithms more efficiently, accelerating the development process.

Overall, Python's extensive libraries, ease of use, strong community support, and compatibility with other tools make it a preferred language for data science. It provides a versatile and accessible platform for data manipulation, analysis, visualization, and machine learning, enabling data scientists to extract insights and make data-driven decisions effectively.

How to code?

Coding is simple like to learn a foreign language what we do:
First learn alphabets ----> Words ---->Sentence
Same in Coding
Learn alphabets(symbols)---->Keywords(prewritten words having special meaning)---->Sentence(Rule to write a intruction or program same like why we use comma or fullstop in english)
One thing more that is “Inbuilt Function”.
Inbuilt function is nothing but prewritten lines of code.
For example, imagine you have a program that needs to calculate the average of a list of numbers. Instead of writing your own code to add up the numbers and divide by the count, you can use an inbuilt function like sum() and len() to calculate the sum of the numbers and count the elements in the list, respectively. Then, you can divide the sum by the count to find the average. This saves you time and effort by using the built-in functionality.
Let’s do it:
To do python programming we need its interpreter and an editor where we write our code.
Step1-Go to www.anaconda.com and download anaconda package according to your system and install it.
Step2- Search Jupyter Notebook in your system in it and open it.When window will be opened,click on New option and create new python file.
You will be able to see the cell as shown in figure.

Type the following code and run it:
Explanation:
The input() function is used to receive user input. In this example, we ask the user to enter the length and width of the rectangle. The float() function is used to convert the input to a floating-point number since we want to handle decimal values.

The area of a rectangle is calculated by multiplying its length and width, so we multiply the length and width variables and store the result in the area variable.

Finally, we use the print() function to display the calculated area to the user.

So in output we can see the area of rectangle.Its so simple.Just we have to remember how to use inbuilt function.To run an inbuilt function we have to use round brackets.
This example demonstrates the basics of accepting user input, performing calculations, and displaying output in Python. Feel free to explore more complex concepts and expand your coding skills from Theta Academy.