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Finding the Correlation | Extracting Data
Advanced Techniques in pandas
course content

Course Content

Advanced Techniques in pandas

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data
2. Dealing With Conditions
3. Extracting Data
4. Aggregating Data
5. Preprocessing Data

bookFinding 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

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

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Section 3. Chapter 7
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bookFinding 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

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 desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 7
toggle bottom row

bookFinding 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

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 desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

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

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 desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 7
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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