## Pandas Drop Duplicates, Explained

This tutorial will show you how to use Pandas drop duplicates to remove duplicate rows from a dataframe. The tutorial will explain what the technique does, explain the syntax, and it will also show you clear examples. You can click on any of the following links, and...

## How to Use np.random.uniform

In this tutorial I'll show you how to use the np.random.uniform function (AKA, Numpy random uniform). I'll explain what the function does, explain the syntax, and show you clear examples of how the function works. If you need something specific, you can click on any...

## Numpy Argmax, Explained

This tutorial explains how to use the Numpy argmax function. It explains the syntax of np.argmax, and also shows step-by-step examples. You can click on any of the links below, and it will take you to the appropriate section of the tutorial. Table of Contents:...

## How to Use Pandas Unique to Get Unique Values

In this tutorial I'll show you how to use the Pandas unique technique to get unique values from Pandas data. I'll explain the syntax, including how to use the two different forms of Pandas unique: the unique function as well as the unique method. (There are actually...

## How to Use the Pandas Assign Method to Add New Variables

In this tutorial, I'll explain how to use the Pandas assign method to add new variables to a Pandas dataframe. In this tutorial, I'll explain what the assign method does and how it works. I'll explain the syntax, and I'll show you step-by-step examples of how to use...

## How to Use Numpy Round

In this tutorial I'll explain how to use the Numpy round function (AKA, np.round). I'll explain how the function works, and I'll also show you some step-by-step examples of how to use it. Table of Contents: A Quick Introduction to Numpy Round The syntax of np.round...

## The 3 Reasons You Should Learn R for Data Science

One of the most common questions I get from data science students is which programming language should I learn for data science, R or Python? The short answer is "it depends." Both R and Python have strengths and weaknesses as data science languages, or as broader...

## A quick introduction to dplyr

There's sort of an open secret in the data science world: As a data professional, you'll spend a huge amount of time doing data preparation. Cleaning, joining, reshaping, aggregating ... These tasks make up a huge amount of your data work. Many data professionals say...