Advanced Techniques in pandas

1. Get Familiar With Indexing and Selecting Data

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.

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

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.

Set Condition

Select Data Based on Condition

Dealing With Several Conditions

Make Your Code Beautiful

Conditions Quiz

3. Extract Data

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.

Is Data in ...?

Combine Your Knowledge

Between Function

Extract Specific Data

Find the Smallest Values of a Column

Find the Largest Values of a Column

Find the Correlation

4. Aggregating Data

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.

Get Familiar With the .groupby() Method

Group by Several Columns

Complicated Grouping

Group

Dealing With Pivot Tables

Create a Pivot Table

5. Preprocessing Data

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.

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

Find the Correlation

Finally, let's move to the last function of this section called .corr(). It helps out a lot to find the relationship between numerical data. Imagine that you have a dataset on houses:

Price USD	Number of Rooms	Distance from the City Center in km
329000	4	25
8739000	6	3
1268000	6	2
987000	4	10
103000	2	30

Let's examine the output of the data.corr() in our case:

	Price USD	Number of Rooms	Distance from the City Center in km
Price USD	1.000000	0.625651	-0.589396
Number of Rooms	0.625651	1.000000	-0.908600
Distance from the City Center in km	-0.589396	-0.908600	1.000000

So, let's do it step by step: You have vertical and horizontal values; each pair overlaps. In each overlap, we can receive a value from -1 to 1.

1 means that two values depend on each other in a directly proportional way (if one value increases, the other increases too);
-1 means that two values depend on each other in an inversely proportional way (if one value increases, the other decreases);
0 means that the two dependent values aren't proportional.

Task

You'll end this section with an effortless task: apply the .corr() function to the dataset. Then, try to analyze the numbers you get.

Everything was clear?

Section 3. Chapter 7

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 Select Data Based on Condition Dealing With Several Conditions Make Your Code Beautiful Conditions Quiz

3. Extract Data

Is Data in ...?Combine Your Knowledge Between Function Extract Specific Data 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 Create a Pivot Table

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

Find the Correlation

Finally, let's move to the last function of this section called .corr(). It helps out a lot to find the relationship between numerical data. Imagine that you have a dataset on houses:

Price USD	Number of Rooms	Distance from the City Center in km
329000	4	25
8739000	6	3
1268000	6	2
987000	4	10
103000	2	30

Let's examine the output of the data.corr() in our case:

	Price USD	Number of Rooms	Distance from the City Center in km
Price USD	1.000000	0.625651	-0.589396
Number of Rooms	0.625651	1.000000	-0.908600
Distance from the City Center in km	-0.589396	-0.908600	1.000000

So, let's do it step by step: You have vertical and horizontal values; each pair overlaps. In each overlap, we can receive a value from -1 to 1.

1 means that two values depend on each other in a directly proportional way (if one value increases, the other increases too);
-1 means that two values depend on each other in an inversely proportional way (if one value increases, the other decreases);
0 means that the two dependent values aren't proportional.

Task

You'll end this section with an effortless task: apply the .corr() function to the dataset. Then, try to analyze the numbers you get.

Everything was clear?

Section 3. Chapter 7