It should be noted that it isn't convenient to check each value of the dataset for the NaN. It is more convenient to see the number of missing values to conclude columns where we have NaNs.
As you remember, we have two functions to check for the missing values. To calculate the sum, just use the `.sum()` function. Thus, in general, we have 4 options for outputting the number of NaNs for each column:

```
data.isna().sum()
# Or
data.isnull().sum()
```

Okay, nothing complicated. Let's move on the task.

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. 