Course Content

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data

Outputting Columns by Title Outputting Rows by Index Selecting Specific Rows and Columns Learning More About Indexation Getting Familiar With lambda Functions Expanding Functionality of the .iloc[] Attribute

2. Dealing With Conditions

Setting Condition Selecting Data Based on Condition Dealing With Several Conditions Making Your Code Beautiful Conditions Quiz

3. Extracting Data

Is Data in ...?Combining Your Knowledge Between Method Extracting Specific Data Finding the Smallest Values of a Column Finding the Largest Values of a Column Finding the Correlation

4. Aggregating Data

Getting Familiar With the .groupby() Method Grouping by Several Columns Complicated Grouping Advanced Grouping Dealing With Pivot Tables Creating a Pivot Table

5. Preprocessing Data

Checking for Missing Values Calculating the Number of Missing Values What Will We Do With the NaN Values?How to Delete Only NaN Values?Filling In the Missing Values Managing Categorical Variables Checking the Column Type Managing an Incorrect Column Renaming the Column

Finding the Correlation

Finally, let's move to the last method 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:

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

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.

Note

If the dataset contains non-numeric columns, such as in the cars.csv dataset used in the task, you should set the argument numeric_only=True to compute the correlation using only the numeric columns.

Task

Swipe to show code editor

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 7

Finding the Correlation

Finally, let's move to the last method 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:

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

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.

Note

If the dataset contains non-numeric columns, such as in the cars.csv dataset used in the task, you should set the argument numeric_only=True to compute the correlation using only the numeric columns.

Task

Swipe to show code editor

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 7

Finding the Correlation

Finally, let's move to the last method 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:

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

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.

Note

If the dataset contains non-numeric columns, such as in the cars.csv dataset used in the task, you should set the argument numeric_only=True to compute the correlation using only the numeric columns.

Task

Swipe to show code editor

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Finally, let's move to the last method 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:

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

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.

Note

If the dataset contains non-numeric columns, such as in the cars.csv dataset used in the task, you should set the argument numeric_only=True to compute the correlation using only the numeric columns.

Task

Swipe to show code editor

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 3. Chapter 7

Switch to desktop for real-world practiceContinue from where you are using one of the options below