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:

||**PassengerId**|**Survived**|**Pclass**|**Name**|**Sex**|**Age**|**SibSp**|**Parch**|**Ticket**|**Fare**|**Cabin**|**Embarked**|
|---|---|---|---|---|----|----|---|---|---|---|---|---|
|0|892|0|3|Kelly, Mr. James|male|34.5|0|0|330911|7-8292|NaN|Q|
|1|893|1|3|Wilkes, Mrs. James (Ellen Needs)|female|47.0|1|0|363272|7-0|NaN|S|
|2|894|0|2|Myles, Mr. Thomas Francis|male|62.0|0|0|240276|9-6875|NaN|Q|
|3|895|0|3|Wirz, Mr. Albert|male|27.0|0|0|315154|8-6625|NaN|S|
|4|896|1|3|Hirvonen, Mrs. Alexander (Helga E Lindqvist)|female|22.0|1|1|3101298|12-2875|NaN|S|


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.

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


Please select the INCORRECT ways of checking for missing values.

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. 

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	892	0	3	Kelly, Mr. James	male	34.5	0	0	330911	7-8292	NaN	Q
1	893	1	3	Wilkes, Mrs. James (Ellen Needs)	female	47.0	1	0	363272	7-0	NaN	S
2	894	0	2	Myles, Mr. Thomas Francis	male	62.0	0	0	240276	9-6875	NaN	Q
3	895	0	3	Wirz, Mr. Albert	male	27.0	0	0	315154	8-6625	NaN	S
4	896	1	3	Hirvonen, Mrs. Alexander (Helga E Lindqvist)	female	22.0	1	1	3101298	12-2875	NaN	S

Select a few correct answers