Summary  
This chapter demonstrates how to detect missing values in a dataset by using functions that return boolean indicators for NaNs.  

General domain of usage  
Data cleaning

I'm happy to see you in the last section of the course. Here, you will process data on the passengers of the Titanic. First, let's examine it:

The first step of our learning is finding missing values. By the way, sometimes it is difficult or even impossible to fill all the values of the column; some of them may be missing. Such cases can spoil your result. In the dataset, they always look like this: `NaN`. First, let's find out if your data set contains missing values.

Pandas has two functions you can apply to the dataset to find missing values. Both of them will put `False` if the dataset values aren't missing, and `True` otherwise.

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


Please select the INCORRECT ways of checking for missing values.

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. 

Checking for Missing Values

Awesome!

Checking for Missing Values