Python Tutorial: Locate rows: .iloc[] and .loc[]

Want to learn more? Take the full course at https://learn.datacamp.com/courses/writing-efficient-code-with-pandas at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Welcome back! In the previous lesson, we studied the basics of timing, how to time a chunk of code, and why speed efficiency matters with pandas and in general. In this lesson, we will look at the loc[] and iloc[] pandas functions, and find out which one is the most efficient to select columns and rows in a pandas DataFrame. Let's look at the main dataset we will use in this lesson, which derives from the famous poker card game. In each round, each player has five cards in hand, each one characterized by its symbol, which can be either hearts, diamonds, clubs or spades, and its rank, which ranges from 1 to 13. The dataset consists of every possible combination of five cards one person can posses. Let's take for example the first combination, which corresponds to the first row. We have a 10 of diamonds, a Jack and a King of clubs, a 4 of spades and an ace of hearts. If you are still not completely sure about the dataset, pause the video and look the bottom part of this slide carefully. One of the most useful features of the pandas library is the ease of convenience of selecting specific rows of a Pandas DataFrame. We're going to use iloc[], the index number locator, and loc[], the index name locator. In this example, we want to select the first 500 rows of the poker dataset. Firstly by using the loc[] function, and then by using the iloc[] function. While these two methods have the same syntax, iloc[] performs almost 200% faster than loc[]. iloc[] takes advantage of the order of the indices, which are already sorted, and is therefore faster. We used iloc[] and loc[] to target rows, but we can also use them to locate different features in a pandas DataFrame. In this example, we want to select the first three columns of the poker