Summary  
This chapter introduces integer-location based indexing using the `iloc[]` attribute to extract rows (and columns) by their positional indices, including support for negative indices to select from the end.  

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?

This course contains a lot of useful functions for a future data analyst. You will learn different ways of extracting data and even set conditions on it. After it, you will be familiar with the methods of grouping data. Also, you will learn how to preprocess data. Each section has its data set so that the course will be gripping.

This section will teach you how to output specific columns by their titles or indices. Also, you will get acquainted with the ways you can select rows  by indices.

Here, you will learn how to extract data that has specific conditions. Also, you will learn how to combine them and even create your own.

In this section, you will expand your knowledge on setting different data conditions. You will learn to check if your data is in a defined list of values or between two values. You will also learn how to find the largest and smallest values.

This section is one of the most fascinating of the course. Here, you will learn how to group data in different ways. It will help you work as a data analyst to find out information on specific data groups.

This section is one of the most significant for a data analyst because if the data contains missing data values in the incorrect format, it will be impossible to work with. Thus, you will learn how to deal with such inappropriate values here. 

Learning More About Indexation

Awesome!

Learning More About Indexation