First, I'll show you how to use these tables to find the cumulative probability for any z-score from −4 to 4. Then we'll create them in Python using NumPy, SciPy, and pandas.
In this article, we'll dive deep into z-score and its practical applications, such as comparing data points across similar distributions, detecting outliers, etc.
You’ll gain an intuitive understanding of this fundamental data analysis technique. By the end, you'll be ready to dive into linear regression, the first ML algorithem most people encounter.
You'll learn how to create a Random Number Generator (RNG), generate samples from various statistical distributions, create random subsets, shuffle arrays, and much more.
You'll also learn to specify component props, set the default prop values, and define prop types.
You'll learn how to do conditional rendering, apply CSS styles, render arrays, handle events, and much more.
You'll also learn how to set up the essential command line tools locally and how to use them.
You'll also get a glimpse into creating and using components to compose a web page, and how to use React Developer Tools to debug your app.
First, I'll introduce the two types of DataFrame indexes. Then, I'll show you how to use the methods iloc[] and loc[] to access the DataFrame data.
These methods provide additional functionality for DataFrames. We'll cover that as well.