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Find the Correlation | Extract Data
course content

Course Content

Advanced Techniques in pandas

Find the CorrelationFind 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
toggle bottom row
course content

Course Content

Advanced Techniques in pandas

Find the CorrelationFind 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
toggle bottom row
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