What is Python and Why you should Learn?

Python is a high-level programming language that is used for data analysis, scientific computing, visualization, and artificial intelligence applications. Python was developed in 1991 and is open-source software released under BSD license. It was initially designed to help developers create tools for interacting with computers in simple ways. Today, it’s used for creating web applications, desktop apps, mobile apps, games, libraries, and much more. As one of the topmost languages in use today, Python covers almost everything. Even though the syntax may seem complex at first, learning this language is rather simple because it's based on English alphabets. If you're interested in coding or want to learn more about Python, read on!

There is multiple reason for Learining Python

• Python is a high level programming language that is used for data analysis and machine learning.
• Python is free and open source software.
• Python is cross platform (runs on Windows, Mac OS X, Linux).
• Python is object oriented.
• Python is a dynamic language.
• Python is interpreted.
• Python is multi-paradigm.
• Python is simple to learn.
• Python is widely used in scientific computing, web development, artificial intelligence, and data science.
• Python is popular among developers.
• Python is used for scripting.
• Python is great for beginners.
• Python is good for self study.

Python Basics

Python is a language that is designed to encourage clear code. There are some things in python that aren't intuitive at first glance. Here are some basics:

a. variables - In python we have a variable called 'x', which holds a value. You can add values to x using "+". So if I had x15, then I could change its value with x+10. If you want to get a number out of x you would just do print(x) to make the computer read the value out.

b. Lists - A list is a way to store a bunch of data and then operate on it. To create a list, put parentheses around the information you want to save and then use the comma operator to separate each item. Each item after the comma is called a sublist. An example would be something like:

numbers

I could access the second item in my list by doing numbers. This means take any item in my list and index it by 1.

c. Functions - A function is a section of code that does something. It's useful because it lets us reuse the same code over and over again. Let's say I wanted to find out how many times the letter 'e' appears in my list. I could write a function that counts up the occurrences of e and return it.

d. Modules - When we import a module, we're saying that we want to use some functionality inside that module. If we were to try importing the modules before they existed, we'd get an error message. We can avoid this problem by writing our own versions of the modules instead of trying to use them directly. Just like a function, we'll need to give the file a name. e. Strings - A string is a sequence of characters enclosed by quotes. When someone types a quote, the end of what was typed gets marked by two consecutive blank spaces. These marks act as delimiters between the string. As long as everything is enclosed in quotes, the string is valid.

f. Classes - A class is a collection of objects with similar characteristics. Think of it like a recipe book. You might have a class called RecipeBook that contains a bunch of recipes. A recipe is an object that belongs to the RecipeBook class.

g. Objects - An object is a container for data. This includes lists, dictionaries, strings, etc. Each piece of data inside an object has a unique identifier (object id). When we need to reference that data later, we use the id. In python, we often create these containers in curly brackets { }.

h. Inheritance - Inheritance is a feature of classes that allows one class to inherit features from another class. So if we have a class named Dog, we could have another class named BarkingDog that inherits from Dog. This means BarkingDog has all the methods and attributes of Dog.

i. Exceptions - An exception is a special kind of error that occurs when we try to execute code that we didn't expect to happen. We can catch exceptions by wrapping them in a try block.

j. Iteration - Iterating through something means going through it step-by-step until we've gone through as much as we want. We can iterate through lists, dictionaries, etc by using square brackets or parenthesis ().

This online python coding course uses real-world examples and assignments. You will learn how to program using Python language and do it in practical way.

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1. Python (also known as CPython)
 The Python programming language was designed to make developing software fun and simple while remaining powerful enough to solve real world problems. Python's syntax is similar to BASIC, C, and Java. There is a free version of python called Idle, which provides you with a simple text editor, debugger, documentation browser, and interpreter.

2. iPython
 iPython is an interactive shell for the IPython notebook. IPython is an open-source project that gives scientists and developers powerful tools for scientific computing and data analysis. It includes a rich set of features for exploratory data analysis, statistics, and visualization.
 3. Matplotlib
 Matplotlib is a Python library for publication quality graphics production, written especially for technical communicators and applied mathematicians. Its emphasis is on plotting functions and visualizing data using tight integration with the rest of the Python scientific stack.
 4. NumPy
 NumPy is a fundamental package for scientific computing with Python. It contains arrays and matrices plus many high-level mathematical functions useful for numeric computation. It supports multidimensional arrays even though only 2D arrays are supported by the official API.
 5. Scikit-Learn
 Scikits-learn is a collection of Python modules for machine learning, including classifiers, regression, dimensionality reduction, clustering, and density estimation.
 6. Pandas
 Pandas is a powerful data analysis and manipulation library written in pure Python. It is built on top of Numpy and is highly optimized for both speed and memory usage.
 7. Seaborn
 Seaborn is a statistical toolbox for Python. It offers ready-to-use distributions along with descriptive metrics and visualization methods. Seaborn does not require any external libraries; it works seamlessly with numpy, scipy, and pandas.