We will learn some new features that `iloc[]` provides. The coolest one is that we can specify indices of both rows and columns. This function is similar to `.loc[]`, but the last index of the slicing is exclusive.

Look at the example and the relevant output:

- `data.iloc[1, 2]` - extracts the item located in the dataset's second row and third column. The first index corresponds to the row index, and the second to the column index. Indeed, you can skip one of them.

- `data.iloc[:, 3]` - extracts all values from the rows of the **fourth** column `'IMDb-Rating'`.

- `data.iloc[:, 3]` or `data.iloc[3]` - extracts the `4th` row and all relevant columns.

- `data.iloc[:2, 1:4]` - extracts the first two rows and column with the indices `1`, `2`, `3`.

- `data.iloc[[2,4],[1,3]]` - extracts the **rows** with indices `2`,`4` and columns with the indices `1`, `3`.

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. 