In this article, you will use RISE as a Python jupyter notebook extension for turning it into a live presentation.

Scenario
Your boss/professor tells you to find the largest number in a Python list and present the result in front of the whole employees/classmates. Now there are some rules that you need to follow to impress your boss/professor. They should be able to see both the documentation and code in this presentation. You should execute the code too. You may use any presentation tool of your choice. Now, how would you be able to present your code and ace it at the same time?

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In this article, we will learn and understand how to parse, read, and write JSON in Python. We will see different conversions of JSON into different Python objects along with a bonus

Definition
JSON is the abbreviation for JavaScript Object Notation: one of the easiest data formats for humans being like you and me to read and write. JSON is often declared as a text format that is completely language independent. It is based on a subset of the JavaScript Programming Language Standard ECMA-262 3rd Edition — December 1999.

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In this article, you will know how to use Pandas DataFrame in everyday life.

Before getting started let us do a quick revision, Pandas is a python library that gives elite, and simple to-utilize information structure for data analysis tools for python programming language. Show below is the article which will help you gain your understanding level in the Pandas library. Don’t forget to have a look at it. In fact, this is the official docs. It is worth reading it.

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In this tutorial, let us learn the usage of Global, Local and Nonlocal variables in Python.

First of all, I’m not the one on that image above. I’m just a benevolent writer who is here to talk about one of the most confusing concepts in Python programming “Global, Local and Nonlocal variables”. I know after reading the title you will be like “Why should I even worry about this”. Well, the answer is sometimes not knowing these teeny tiny itsy bitsy would cost you a lot. So without further ado, let’s get started.

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Ever thought about rendering images inside a dataframe? I’m glad you did!

I can see the question mark (?) on most of your faces. Because even I had one before discovering that we can also render images inside a dataframe. And believe me, it’s simple and easy, all you need to know is to create a dataframe and I’ll pick you up from there.

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Just say goodbye to OpenCV, Matplotlib, PIL except for Numpy! Well, the above picture is splendid, isn’t it?. It’s a masterpiece to me. Nonetheless, have you ever thought or asked yourself this one million dollar question:

What is an image made up of?
Now, before reading this entire tutorial, I want you guys to answer this question in the comment section, in that way you can give some work to your brain. Anyway, for those of you never answered the question or didn’t know the answer, by reading this article you can finally answer the question with no hesitation. Enough of this hype, let’s get started!.

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In this tutorial, let us learn the “Bar Plot” visualization in-depth with the help of examples.

Introduction
The data visualization is one of the most important fundamental toolkits of a data scientist. A good visualization is very hard to produce. Often during a presentation, people don’t understand well enough the data, or the statistics involved but showing them a good visualization will help them understand the story we are trying to convey them. Therefore, they say a picture is worth a thousand words.

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