Course Content

Advanced Techniques in pandas

1. Get Familiar With Indexing and Selecting Data

Output Columns by Title Output Rows by Index Select Specific Rows and Columns Learn More About Indexation Get Familiar With lambda Functions Expand Functionality of the .iloc[] Function

2. Dealing With Conditions

Set Condition Challenge 1 Dealing With Several Conditions Make Your Code Beautiful Challenge 2

3. Extract Data

Is Data in ...?Combine Your Knowledge Between Function Challenge Find the Smallest Values of a Column Find the Largest Values of a Column Find the Correlation

4. Aggregating Data

Get Familiar With the .groupby() Method Group by Several Columns Complicated Grouping Group Dealing With Pivot Tables Challenge

5. Preprocessing Data

Check for Missing Values Calculate the Number of Missing Values What Will We Do With the NaN Values?How to Delete Only NaN Values?Fill In the Missing Values Manage Categorical Variables Check the Column Type Manage an Incorrect Column Rename the Column

Advanced Techniques in pandas

Check for Missing Values

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.

Please select the INCORRECT ways of checking for missing values.

Select a few correct answers

Everything was clear?

Section 5. Chapter 1