Summary  
This chapter explains how to use the DataFrame.iloc attribute for integer-location based indexing to extract specific rows (and columns) by their positive or negative indices.

General domain of usage  
Data analysis

Let's move further and continue extracting columns and rows by indices. So, you need to be familiar with a attribute similar to `loc[]`.

Our following attribute is `iloc[]`; it is like **index-location**, and you may have guessed that it allows us to work with both columns' and rows' indices.

Firstly, we need to recall indices. The **first** row has the index `0`, the next one `1`, the next `2`, and so on. But also, we can count from the end (indeed, this is not convenient in datasets, but it may be helpful somehow), so the **last** one has the index `-1`, the **second-to-last** is `-2`, and so on...

Look at the table:

However, we will start with the simplest implementation of the attribute `iloc[]`, working with the following data set (below are its first five rows):

Look to the code example and output:

- `data.iloc[0]` - extracts the **very first** row of the dataset;
- `data.iloc[1]` - extracts the **second** row of the dataset;
- `data.iloc[-1]` - extracts the **very last** row of the dataset;
- `data.iloc[-2]` - extracts the **second-to-last** row of the dataset.

As you may have recognized, at the end of the output, the variable `Name` shows the row number too, like `Name: 998`.
## Question
**Replace the placeholders** `___` **in a code window with your code to answer the question below it**.

import pandas as pd
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/people.csv')

print(data.___) # CHANGE CODE HERE (to answer the question below)
print(data.___) # CHANGE CODE HERE (to answer the question below)

Note that the index of a first person is **0**.

Extract the second and the last row in the code window above. What are the names of those persons?

Indexation

Question